International Journal of Occupational Safety and
Ergonomics
ISSN: 1080-3548 (Print) 2376-9130 (Online) Journal homepage: www.tandfonline.com/journals/tose20
Towards a conceptual framework of OSH risk
management in smart working environments
based on smart PPE, ambient intelligence and the
Internet of Things technologies
Daniel Podgórski, Katarzyna Majchrzycka, Anna Dąbrowska, Grzegorz
Gralewicz & Małgorzata Okrasa
To cite this article: Daniel Podgórski, Katarzyna Majchrzycka, Anna Dąbrowska, Grzegorz
Gralewicz & Małgorzata Okrasa (2017) Towards a conceptual framework of OSH risk
management in smart working environments based on smart PPE, ambient intelligence
and the Internet of Things technologies, International Journal of Occupational Safety and
Ergonomics, 23:1, 1-20, DOI: 10.1080/10803548.2016.1214431
To link to this article: https://doi.org/10.1080/10803548.2016.1214431
© 2016 Central Institute for Labour
Protection – National Research Institute
(CIOP-PIB). Published by Taylor & Francis.
Published online: 06 Sep 2016.
Submit your article to this journal
Article views: 14959
View related articles
View Crossmark data
Citing articles: 34 View citing articles
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=tose20
International Journal of Occupational Safety and Ergonomics (JOSE), 2017
Vol. 23, No. 1, 1–20, http://dx.doi.org/10.1080/10803548.2016.1214431
Towards a conceptual framework of OSH risk management in smart working environments
based on smart PPE, ambient intelligence and the Internet of Things technologies
Daniel Podgórski ∗ , Katarzyna Majchrzycka, Anna Dabrowska,
˛
Grzegorz Gralewicz and Małgorzata Okrasa
Central Institute for Labour Protection – National Research Institute (CIOP-PIB), Poland
Recent developments in domains of ambient intelligence (AmI), Internet of Things, cyber-physical systems (CPS), ubiquitous/pervasive computing, etc., have led to numerous attempts to apply ICT solutions in the occupational safety and health
(OSH) area. A literature review reveals a wide range of examples of smart materials, smart personal protective equipment
and other AmI applications that have been developed to improve workers’ safety and health. Because the use of these solutions modifies work methods, increases complexity of production processes and introduces high dynamism into thus created
smart working environments (SWE), a new conceptual framework for dynamic OSH management in SWE is called for.
A proposed framework is based on a new paradigm of OSH risk management consisting of real-time risk assessment and
the capacity to monitor the risk level of each worker individually. A rationale for context-based reasoning in SWE and a
respective model of the SWE-dedicated CPS are also proposed.
Keywords: ambient intelligence; Internet of Things; smart working environment; occupational safety and health management; smart personal protective equipment; cyber-physical systems; real-time risk assessment; ubiquitous safety
List of the most frequently used abbreviations
AmI
ambient intelligence
AR
augmented reality
CPS
cyber-physical systems
ES
embedded system
GPS
global positioning system
ICT
information and communication technologies
IoT
Internet of Things
SNS
smart networked system
OSH
occupational safety and health
PPE
personal protective equipment
RFID
radio-frequency identification
SE
smart environment
SM
smart material
SWE
smart working environment
VR
virtual reality
WSN
wireless sensor network
1. Introduction
The dynamic progress in the domains of electronics and
information and communication technologies (ICT) has
been apparent since the last decades of the 20th century,
and has been characterised by striving for miniaturisation of electronic components and devices, achieving the
mobility of computing systems, and by developing wireless networks consisting of distributed physical objects and
devices, which may function together when connected in
a logically coherent ensemble. At the same time, recent
advances in sensor technologies have led to a state in which
various physical, chemical or spatial characteristics of real
objects and environments can be sensed and measured easily, reliably and at relatively low cost. These advancements
have triggered the emergence of new, somewhat overlapping and inter-related concepts, which are appearing
in the literature under terms such as ambient intelligence
(AmI), smart environment (SE), cyber-physical systems
(CPS), ubiquitous computing, pervasive computing and
the Internet of Things (IoT). The mentioned concepts (see
description in Section 2) are currently beginning to conquer all sectors and domains of life and human activities,
including new manufacturing concepts, such as Industry
4.0 and Smart Factory, as well as other exploration fields
that are in various ways related to occupational safety and
health (OSH).
In general, innovations in the field of ICT have a positive impact on the quality of work and life, lead to the
improvement of functional performance of people and systems, and contribute to profound transformation in modern
societies.[1,2] Together with the development of AmI and
IoT concepts there have been attempts to apply innovative
ICT applications, also in the field of OSH. In particular, this
concerns situations in which direct hazards for health and
life may occur, and in which workers’ protection can often
*Corresponding author. Email: dapod@ciop.pl
© 2016 Central Institute for Labour Protection – National Research Institute (CIOP-PIB). Published by Taylor & Francis.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/
by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed,
or built upon in any way.
2
D. Podgórski et al.
be guaranteed only by means of advanced personal protective equipment (PPE) enriched with novel smart materials
(SMs) and wearable electronics (so-called smart PPE).
Such cases typically include activities performed during
rescue actions, in harsh environments or in workplaces
where complex hazards exist.
First of all, new sensor technologies offer numerous
possibilities for the improvement of OSH by means of realtime monitoring of hazardous and strenuous factors, such
as noise, exposure to toxic chemical substances, optical
radiation and high or low temperature.[2,3] Furthermore,
ICT applications allow facilitating other key functions of
OSH management related to hazard identification and risk
management. Such functions cover, e.g., monitoring workers’ health state by measuring key physiological parameters (i.e., body temperature, heart rate, breathing rate, etc.);
monitoring work comfort (e.g., underclothing temperature
and humidity, work posture); geographical localisation of
workers with regard to other, potentially dangerous objects
or high-risk zones; monitoring the current protection level
provided by PPE; detecting the end-of-service-life of PPE
used by workers; providing warnings to workers in case
of emergence of hazardous situations; and the activation
of protective systems after exceeding a high-risk threshold value.
At the same time, the role of PPE in the management of the working environment has started to change.
Besides being used as a means of passive protection against
hazards, PPE items have also started to be used as carriers of sensors for monitoring work environment parameters, worker’s health status and his or her location in the
workplace space. Another trend has concerned incorporating signalisation systems into the PPE modules, which
enabled displaying warnings to the worker, e.g., information on the occurrence of hazards or instructions on
how to avoid them. Such embedded systems (ESs), often
based on augmented reality (AR) technologies, can also
provide workers with information that is useful for highlyspecialised tasks, such as maintenance, repair or welding.
Moreover, it is also possible to apply wearable electronics
for monitoring PPE’s protection performance level, which
takes into account current hazardous factors in a given
workplace. Such functionality allows for the identification and prediction of changes of protective parameters’ of
PPE, which may occur under the influence of environmental factors. Furthermore, embedding SMs into PPE items
enables quick change of their protective and utility parameters, as well as active adjustment of their properties to
the environmental conditions and the individual worker’s
requirements.
On the other hand, the implementation of new ICT
technologies in the working environment leads to significant changes by modifying methods of work and introducing new objects and complex systems that may have
functions which are not fully recognised, and can be characterised by a certain degree of uncertainty while receiving
and processing signals from sensors. As a consequence,
such systems may not function according to users’ expectations and may be subject to unforeseen failures and
consequences. The rapid increase of ICT-based applications may thus lead to the emergence of new risks, and may
consider existing OSH solutions and rules to be invalid.
This is why the European Agency for Safety and Health
at Work (EU-OSHA) and the Partnership for European
Research on Occupational Safety and Health (PEROSH)
call in their reports [2,3] to undertake research studies to
explore the possibility of using advanced ICT solutions for
creating systems ensuring the safety and health of workers in smart working environments (SWEs), and to analyse
the impact of these solutions on workers with various skill
levels, cognitive abilities and psychological states.
Implementation of advanced ICT solutions in the OSH
field, as well as the emergence of the concept of Smart Factory, are associated with significant conceptual challenges
that consist mainly of maladjustment of existing laws and
practices, especially of current formalised approaches to
OSH risk assessment, to broad and yet not fully recognised
functional capabilities of ICT applications. In particular,
because of such novel functionalities as real-time hazard
detection and risk monitoring, context awareness and a personalised approach to risk assessment, the existing static
models of risk management become insufficient for the
SWE. Therefore, the main objective of this article is to
present a proposal for a conceptual framework depicting
management of OSH risks with regards to workers who
are equipped with smart PPE, and whose workplaces are
covered with CPS infrastructures that implement safety
functions within the SWE (this framework is given in
Section 4). An intermediate objective is to provide an
overview of state-of-the-art concepts, SMs, technologies
and advanced ICT-based solutions that can be further
exploited and researched, with the overarching aim to build
a CPS capable of effective performing of OSH risk management functions in the SWE (see Sections 2 and 3).
Finally, several practical issues related to implementation
of CPS and SWEs are discussed in Section 5, and a vision
of ubiquitous safety is proposed in Section 6. The latter
aims to provide a concise set of objectives and principles that could drive and promote further research and
development in the area of SWE.
2. Basic notions and underlying concepts
The term ‘ambient intelligence’ (AmI) was first used in
1998 by Philips company experts and consultants of Palo
Alto Ventures.[4] According to a synthetic definition proposed by Augusto [5,p.215], AmI is a ‘digital environment
that proactively, but sensibly, supports people in their daily
lives’. Functionality of AmI is based on the ability to use
many inter-related technologies, which, in respect of their
basic functions, can be assigned to five areas: (a) sensing;
International Journal of Occupational Safety and Ergonomics (JOSE)
(b) reasoning, (c) acting; (d) human–computer interaction;
and (e) security and privacy.[6]
The next notion – the ‘smart environment’ (SE) – refers
mainly to concrete spaces, and to the physical objects
located in those spaces, which are subject to intentional
actions of AmI systems, and act with an overarching objective, to adapt this environment (preferably in real-time) to
specific needs and experience of its users (the notion of
users refers here to both human participants and technical objects). SE parameters are monitored and processed
by underlying computer applications, which may influence
this environment in a responsive manner.[7] The notion of
the SE is very often used in literature in reference to smart
living or smart homes, because the first practical applications of AmI technologies have been employed in these
fields. However, the term ‘smart working environment’
(SWE) is used in this article in relation to a geographical
location of a workplace and a complex of surrounding conditions, in which employees perform work-related tasks,
and in which the monitoring of environmental parameters
and the interaction between workers and physical objects
(e.g., machinery, working tools, safety devices, PPE) is
supported by respective AmI technologies.
In turn, the notion of ‘cyber-physical systems’ (CPS),
refers to embedded intelligent ICT systems that are interconnected, interdependent, collaborative and autonomous,
and which provide computing and communication, monitoring/control of physical components/processes in various
applications.[8] In terms of semantics, the CPS is a sort
of ‘engine’ of the SE that implements computing function and ensures communication, monitoring and control,
so a given environment may function in a ‘smart mode’. In
general, the concept of CPS can be illustrated as an abstraction infrastructure composed of two inseparably interconnected layers: a physical one and a cyber one (Figure 1).
The physical layer consists of physical objects that are
located in real 3D space, perform specific tasks and interact
physically with each other. These objects may be equipped
with appropriate sensors and actuators, or may themselves
3
constitute such sensors and actuators. The examples of
objects of a physical layer, which are symbolically presented in Figure 1, are workers and various machines that
may interact with each other in certain smart workplaces,
whereas a cyber layer consists of a network of spatially
distributed computing and communication nodes, which
are directly connected to sensors or actuators immersed in
the physical environment. It is assumed that these sensors
and actuators are respectively equipped with transmitters,
receivers and data processing units, thus ensuring the flow
and processing of data in order to monitor and control a
given SE.
The next two notions, which are commonly used in the
context of SE applications, are ‘ubiquitous computing’ and
‘pervasive computing’. The use of the term ‘ubiquitous
computing’ is typically associated with an article published
by Weiser,[9] which was devoted to a vision of ICT developments for the 21st century. The second term gained popularity as a result of IBM’s efforts in promoting the vision
of pervasive computing.[10] Both of these notions have
basically the same meaning,[11] and refer to a trend oriented towards embedding microprocessors and other intelligent chips into any object that surrounds human beings
on a daily basis, e.g., homes, cars, tools, machines, clothing
and even into the human body. The objective of this trend is
to achieve such a level of integration of computing devices
into the objects of daily life, until they are indistinguishable
from these objects and invisible to the users.
Recently, the implementation of ubiquitous/pervasive
computing has become closely connected with the idea
of the IoT, in which unambiguously identifiable objects
(things) may directly or indirectly collect, process and
exchange data amongst themselves and among the objects
and humans by means of computer networks, particularly
via the Internet. Both the concept and its descriptive phrase
‘Internet of Things’ were first proposed in 1999 by K.
Ashton [12] of the Massachusetts Institute of Technology (MIT), in the context of the application of radiofrequency identification (RFID) technology for supply
Figure 1. Graphical visualisation of two abstraction layers of cyber-physical system infrastructure in the workplace.
4
D. Podgórski et al.
chain management. Since then, the scope of IoT-related
applications has improved drastically, and is expected
to soon dominate all aspects of human life and professional activity.
The vision of the IoT is based on the assumption that
interconnected objects should be: identifiable (everything
identifies itself); able to communicate (everything communicates); and able to interact (everything interacts).[13]
These objects include practically everything that people
may use in homes, at work, at leisure time, as well as
in the healthcare, environmental protection, public lives,
etc. Only a small amount of potential IoT applications
are presently available, as objects covered by these applications communicate poorly with each other and are
equipped with rather primitive intelligence.[14] However,
the scope of possible applications of IoT is so enormous
that, at present, all we can do is to speculate about the
domains in which IoT solutions will be used in the near
future. The IoT is growing exponentially and it is expected
to reach ca. 26 billion connected devices worldwide by
2020.[15] Thus, the IoT will create more and more technological capabilities for the development of SEs with much
more sophisticated functions.
The concept of the IoT and a growing number of
advanced ICT applications in modern industry are associated with the 4th industrial revolution, which in Europe is
often referred to as ‘Industry 4.0’.[16] This stage of industrial development is usually confronted with the previous
ones, i.e., the 1st industrial revolution that took place in
the 18th century and was connected with the invention
of steam engine and an introduction of mechanisation of
work; the 2nd, which began in the late 19th century and
was associated with the use of electricity, implementation
of mass production techniques and a division of labour (socalled Taylorism); and the 3rd revolution, which began in
the last few decades of the 20th century, and was based on
the use of electronic systems and information technologies
for the automation of production processes.[17,18] The
final goal and the essence of the 4th industrial revolution
is the idea of Smart Factory,[16] usually understood as a
manufacturing system equipped with a context-awareness
functionality that assists people and machines in execution
of their tasks. Smart Factory will be able to self-organise
and adapt itself in real time to any changes and emergency situations, will efficiently use available resources
and energy, and will allow for effective use of knowledge, expertise and innovation potential of employees.
Such a factory should ensure rapid product customisation
and be able to deliver single product items complying with
diversified needs of individual customers.
3.
A literature review of SMs, smart PPE and other
OSH-related ICT solutions
3.1. Smart materials embedded into PPE
So-called SMs, understood as materials that are able to
react to environmental stimuli by means of a significant
change of their properties for a desired and effective
response,[19] have played an essential role in technological
progress and expanding a field of smart PPE applications.
Implementation of materials that are able to act as both
sensors and actuators allowed for a functionalisation of
PPE towards completely new properties that could never
be achieved with the use of traditional materials.
Recently, the market notes a strong tendency to provide PPE with thermoregulation properties. Phase change
materials (PCMs) and super-absorbing polymers (SAPs),
in particular, are used for managing thermal stress. PCMs
are able to store and release a certain amount of heat in
the form of latent heat in a specific temperature range.[20]
This ability has been used, e.g., in a thermoregulation
vest for a reduction of thermal discomfort related to using
impermeable protective clothing,[21] as well as in work
in hot and cold environments.[22,23] SAPs can absorb
and retain extremely large amounts of liquid in relation
to their weight.[24] In order to prevent liquid sweat on
the skin, which highly influences human sensations, SAP
inlays were applied under the tight protective clothing.[25]
SAPs were also used to develop textile composite materials
for insoles to permeation-resistant protective footwear.[26]
Integration of both of these SMs into PPE can be achieved
inter alia by means of a wide variety of finishing chemicals for textiles that allow converting textile materials into
a technical textile with functional properties.[27]
Novel functionalities of PPE can also be achieved by
application of SMs that generate electricity as a consequence of certain optical (photovoltaic effect), mechanical (piezo-electricity) or thermal (Seebeck effect) stimuli.
They are used in smart PPE as solar cells,[28] as well as a
source of electricity gathered from the deformations resulting from body movement [29] or from the temperature gradient between the human body and the environment.[30]
Another group of SMs responds by changing optical
(electrochromism), mechanical (electrostriction), chemical (electrolysis) or thermal characteristics (Peltier effect)
when powered by electric current.[31] These materials
have been used, e.g., as actuators in cooling–heating protective clothing [32] or for user interfaces based on helmetmounted displays.[33] However, in order to enable proper
functioning of these SMs, other elements are required (e.g.,
power sources, sensors and control unit). This group of
SMs is therefore usually not used as an autonomous solution, but as part of ICT-based systems embedded into the
PPE.
3.2.
Wearable electronics in PPE applications
According to the most common definition, wearable electronics is a device that in addition to its primary function is
always attached to a person and is easy and comfortable
to keep and use.[34] One of the first works that mentions wearable electronics investigates materials, devices,
software and incorporation methods to facilitate integration of electronics into textiles.[35] Recent advances
International Journal of Occupational Safety and Ergonomics (JOSE)
in miniaturisation of ICT technologies have led to an
increased interest in wearable electronics, which resulted
in the development of a range of solutions, also in the field
of PPE, starting from simple wearables containing sets of
individual sensors and actuators, and ending with highly
specialised ESs, i.e., computing devices incorporated into
larger products in order to perform specific advanced
functions.[36] Various sensors that collect information on
selected environmental parameters ensure the cooperation
of such systems with the SE. This information is processed
accordingly and transferred to actuators, enabling the system to react immediately to changes of external conditions,
e.g., by means of a modification of its properties.[19] Their
non-standard hardware, software and user interfaces are
designed in a way that ensures specific functionalities, as
well as high performance, reliability and low consumption
of resources at the same time. Usually, OSH-related ESs
are integrated with PPE items, and are dedicated to one
particular safety-related or comfort-related function, e.g.,
workers’ localisation in relation to high-risk zones,[37]
active control of PPE properties depending on the working environment conditions [38] or monitoring workers’
physiological parameters.[39]
One of the first areas of application of electronic
wearables, used not only in the working environment,
but also for medical and sport purposes, is in human
activity and health/physiological status monitoring systems. Such systems usually employ strain gauges for
respiratory monitoring, body temperature sensors, heart
rate monitors, ECG (electrocardiography) sensors [40]
or microwave frequency flexible e-textiles for real-time
health monitoring.[41] Currently, there are many types of
ESs that allow monitoring of various physiological parameters, including body posture, muscle activity, blood pressure, skin conductance, movement, oxygen level, hydration, temperature, brain activity, glucose, eye tracking,
sleep, respiration, ingestion and heart tracking.[42] For
example, an RFID passive ultrahigh-frequency (UHF) epidermal sensor is proposed to be directly attached onto the
human skin in order to ensure a real-time and continuous wireless measurement of human body temperature.[43]
Witt et al. [44] developed an optic fibre sensor to be
incorporated into the PPE, which enables measurement of
arm pulse and natural movements accompanying breathing and heart beat. Within the ConText project, contactless
textile-based sensors were developed. They register muscle
activity of the user and collect information on physiological stress levels, which could be further used to lower
the risk of musculoskeletal disorders.[45] Another interesting solution in this field is a wearable computer integrated
with a standard safety helmet to protect workers from carbon monoxide poisoning by continuous and non-invasive
monitoring of blood gas saturation levels.[46]
Apart from monitoring physical activity and workers’ health status, various sensory modules can be integrated with PPE to enable continuous measurement of
environmental conditions in the direct vicinity of the
5
worker. An example of this solution is a smart PPE
system dedicated for firefighters, chemical rescuers and
mine rescuers, which was developed within the i-Protect
project.[47,48] The system comprises of sensors measuring the concentration of six different gases and a communication network ensuring that measured data are transmitted
wirelessly to the rescue coordination centre. Similar issues
have been taken up within the PROeTEX project.[49] In
this project, protective clothing for firefighters has been
integrated with a Body Area Network that includes: a module for monitoring rescuer activities based on global positioning system (GPS) signal and accelerometers, heat flux
and gas sensors for the assessment of chemical and thermal
hazards, and long-distance and short-distance communication modules providing necessary information support
for the rescue team members.[50] Next, the SafeProTex
project was focused on enhancing the protection level
of clothing intended to protect people exposed to hazards in complex operations and emergency situations, e.g.,
firefighters and first aid medical personnel during operations in extreme weather conditions or under the risk of
wild fires.[51,52]. In another project, PROSPIE, the main
objective was to improve firefighters’ comfort by means
of the reduction of thermal stress during exposure to a
hot microclimate. For this purpose, a system for monitoring a worker’s thermal state as well as cooling agents
such as cooling salts and PCMs were considered, along
with ventilation-based cooling.[53] Within the ProFiTex
project, a smart jacket was developed with integrated sensors, electronics and a security rope acting as a medium
to transmit data and energy. This project was focused on
ensuring a navigation support for firefighters in smoky
zones, as well as on exchange of information between
firefighters and a commander by means of embedded
beacons.[54]
Wearable electronics has been also integrated within
the protective gloves for firefighters. For some work along
these lines, see [55,57], where the authors proposed firefighter gloves with an embedded wireless system for
temperature measurement, haptic feedback and gesture
recognition in order to support workers with heat-related
warnings and messages. In particular, the system integrated
the following sensors: two accelerometers for the interpretation of hand signals or immobility; an analogue temperature sensor for monitoring human temperature on the back
of the hand; a thermocouple for fast measurement of contact heat; and a barometer for a detection of atmospheric
pressure changes.[57] Additionally, two miniature vibration motors were embedded in a flexible part of the glove in
order to provide the firefighter with haptic feedback in case
of dangerous situations. This application allowed the generation of various vibration signals the meaning of which
was impulse-length dependent.
The integration of ICT devices with standard PPE items
also offers a great opportunity for implementing real-time
control of protective and comfort-related PPE properties,
as was proposed, e.g., in the HORST project.[58] Within
6
D. Podgórski et al.
this project, the safety of foresters has been improved by
means of replacement of typical protective padding by
highly sensitive magnetic field sensors embedded into cutprotective trousers. Such smart PPE design allows for a
portable chainsaw to be automatically and immediately
stopped when the moving chain is too close to a worker’s
legs. A similar wearable safety device for covering the
body part of an operator of a machine tool was patented
recently.[59] This comprised magnetic sensors and an electronic communication unit configured to emit a wireless
signal that is interrupted if any of the magnetic sensors
detects a magnetic field above a specified threshold value.
When the machine tool does not receive the signal it is
automatically disabled. Within the SOLARTEX project,
flexible solar cells and rechargeable electronics have been
developed for integration into clothing.[60] One application of such a solution was the incorporation of bright
solar-powered lights into high-visibility clothing for street
construction workers. Another example is a liquid-based
cooling system that was designed to reduce thermal stress
in hot environments.[61] The system measures the undergarment microclimate parameters and controls the temperature of the coolant, and, as a consequence, maintains
a stable comfort level individually adjusted to the user’s
needs. Yet another cooling system integrating miniaturised
fans, ensuring ventilation through the fabric spacer, and
a liquid-filled pad that cools its surroundings due to heat
loss caused by water evaporation was incorporated into an
actively cooled bullet-proof vest.[62] There are also PPE
solutions that employ ESs to reduce the workload related
to manual handling work, such as the case of the CareJack
power vest, designed for caregivers and others with physically demanding jobs to help with heavy load lifting.[63]
The vest stores the kinetic energy of the wearer’s movements and releases it when required, thanks to the electronics incorporated into the textile material. An active
thermo-regulation system was also embedded into protective clothing intended for use in cold environments.[64]
The system automatically adjusts the user’s thermal comfort to changing weather conditions by controlling electric
power delivered to the heating inserts.
As was mentioned in Section 1, the integration of
wearable electronics into PPE items can also aim at providing a worker with information support connected with
potential hazards or work-related processes. In order to
increase safety of police officers,[56] and to ensure a warning system for rail-track workers,[65] high-visibility vests
with light-emitting diodes were proposed. These smart
PPE solutions were also equipped with embedded vibrators and sound alarms ensuring that, in the case of a
dangerous situation, multiple warning signals could be
generated. Another method to provide workers with information support is the use of AR systems that make it
possible to monitor one’s surroundings, and to introduce
additional information into the user’s field of vision. A
good example of a PPE item that can be integrated with
an AR module is a welding helmet used during manual welding processes. With regard to this, there were
already several solutions developed to provide the welder
with real-time assistance, e.g., with instructions concerning
welding gun positioning.[66]. Similar technical assistance,
complemented with early warning signals generated in a
dangerous situation, can be provided through a fully transparent wearable display, positioned beneath a protective
visor of an industrial helmet equipped with an AR module,
cameras and sensors.[67]
Another issue that can be addressed by integrating
wearable electronics with PPE is monitoring of their life
cycle, as well as a control of the completeness of PPE
items used by a worker. A group of solutions addressing
these issues using RFID tags concerns, e.g., access control
to high-risk areas based on the completeness of PPE,[68]
automated and rapid control of PPE at construction sites
[69] and an automatic identification system for the endof-service life of various types of PPE.[70,71] Another
method to monitor damages of protective functions of PPE
has been employed in the lightweight and flexible protective vest for law enforcement personnel.[72] In this
solution, an array of conductive wire sensors was applied
to the textile substrate, which changes resistance in the case
of damage by ballistic or stabbing attacks.
For more examples and reviews concerning other types
of SMs, sensor technologies and wearable electronics, as
well as potential fields of application, see, e.g., [31,73–75].
3.3. Smart networked systems
Smart networked systems (SNSs) can be defined as a collection of spatially and functionally distributed embedded
computing nodes that are interconnected by means of wired
or wireless communication infrastructures and protocols.
In SNSs, data processing is usually performed on a different layer than in the case of typical ESs. SNSs interact with
each other and with the environment via sensor/actuator
components. Such infrastructure can also include a master
node that conducts coordinative functions and calculations
for the whole SNS.[76]
Communication and data processing units based on
concepts of the IoT, using such wireless communication
infrastructures and protocols as 6LoWPAN, ultra-wide
band (UWB), RFID, wireless sensor networks (WSNs),
real-time locating systems (RTLS), ZigBee and WiFi, have
become an integral part of SNSs.[77,78] There are numerous benefits of using those networks, which include high
flexibility, ease of system installation and maintenance, and
a potential to advance the management of processes by
providing real-time access to workers; locations, materials
and equipment, especially in harsh and dynamic working
environments.[79,80]
International Journal of Occupational Safety and Ergonomics (JOSE)
In the area of OSH-related SNSs, an important role
is played by indoor and outdoor workers’ location systems. They allow for detecting workers’ proximity to highrisk zones, usually by means of GPS, wireless local area
network, UWB, RFIDs and vision systems. An example
of a location system for protecting workers is a prototype of the magnetic sensing system called HASARD
(Hazardous Area Signalling and Ranging Device), which
detects a worker’s presence in the operating zone of underground mining machines.[81] A similar solution based on a
radio-frequency remote-sensing technology was also used
for improving workers’ safety at construction sites.[82].
Another example is a system based on IoT technologies,
namely on WSN and RFID integrated solutions, which
monitors workers’ location and controls safe access to dangerous areas of the workplace, in which safety equipment
is required.[83]
SNSs were also applied to support implementation
of OSH regulations. In this context, an intelligent coal
mine monitoring system based on a Controller Area Network bus and ZigBee technology, intended to monitor the
coal mine production process,[84] can be mentioned as
an example. It enables one to determine miners’ location and generates warning signals for safety managers
when dangerous incidents, such as gas or water leakage,
emerge. A similar system for mining applications enables
the detection of the exact location and spreading direction
of fire.[85] Next, an automatic railway track warning system based on distributed personal mobile terminals was
designed to localise the workers within the work site, alert
them about approaching trains and, in case of emergency,
guide them to a safe area.[86] Other IoT-based SNSs were
developed for real-time monitoring of such parameters as
temperature, humidity, air quality, vibrations of operating
machinery, electrical overload and detection of flames in
the plant,[87] and for combined monitoring of workers’
location and their exposure to such indoor air pollutants
as formaldehyde and CO2 .[88]
Yet another field of SNS applications is the monitoring
of safety and work processes in human–robot collaborative
stands where traditional technologies, e.g., light curtains
and pressure-sensitive safety mats, detecting a worker’s
presence in the operation zone of a robot can be applied. A
more advanced example is a radiolocation-based system,
which consists of radio-frequency transceivers deployed in
the robot’s working zone, making it possible to determine
the operator’s position on the basis of electromagnetic
field perturbations, and to optimise robot movement trajectory so as to minimise the probability of a hazardous
event.[89] Next, the development of 3D image fusion
for human safety in industrial work cells is also worth
mentioning.[90] The systems employ the concept of a personal safety zone that encloses each person and a machine.
Sizes and locations of separate personal zones are dynamically updated based on their movement directions and
7
velocities. Impending collision can be detected when the
zones intersect, and then the trajectory and/or velocity of
the machine can be modified.
3.4. ICT applications devoted to OSH risk assessment
The presented overview demonstrates the main directions
in the development of smart PPE solutions and other ICTbased safety applications in the field of OSH. Notwithstanding the contribution of the presented solutions to the
development of a SWE concept, their utilisation is usually
limited to the application of several sensors, i.e., monitoring selected risk factors, detecting location of workers
and generating warnings for workers approaching highrisk zones, due to relatively simple rule-based algorithms.
Initial attempts to develop an OSH risk monitoring system in the SWE were made within the project FASyS. The
project’s findings have led to a rationale for personalised
risk management in the so-called sensing enterprise,[91]
and to a proposal of reference architecture of an ICT-based
platform aimed at the operation of wireless networks that
connect mobile sensors in industrial environments.[92]
In reference to the idea of personalised risk management, one should also mention attempts at personalised
assessment of OSH risks related to the development of
ORCA (Occupational Risk Calculator), a software tool
which allows for quantified risk assessment for individual
workers or jobs.[93,94] ORCA is based on an occupational risk assessment model developed in The Netherlands
within the WORM Metamorphosis project.[95,96] The
model takes into account various workers’ tasks, activities
and related hazards; however, the method does not cover
monitoring of either workers’ physiological parameters or
environmental factors.
4.
A conceptual framework for OSH risk
management in SWEs
4.1. A new paradigm for risk assessment and
management
The concepts of the assessment and management of risks
related to hazards and/or harmful factors that occur in
the working environment are the foundations of European philosophy and social policy related to the prevention of accidents and occupational diseases.[97] The legal
requirements to carry out OSH risk assessment, and to
implement, on its basis, relevant, protective and preventive measures (further also referred to as risk controls),
result from the OSH Framework Directive, i.e., Directive
89/391/EEC. These principles are obligatory in the European Union (EU), and after many years of their practising,
it is now commonly regarded that risk management is a
key process of an effective company’s OSH management
system.[97,98]
8
D. Podgórski et al.
Current methodologies for OSH risk management conclude that these processes should basically consist of five
steps: (a) identifying hazards; (b) assessing and prioritising
the risks arising from hazards; (c) planning implementation
of risk prevention and control measures; (d) taking actions
to eliminate the risk or reduce it until it is admissible;
and (e) monitoring, reviewing and updating the risks regularly to ensure that implemented measures are adequate
and effective.[99] In typical workplaces in the industry,
where dynamic changes in working conditions are not very
common, risk assessment for a given workstation is carried
out no more frequently than every few months. Although
the legal provisions require that hazard identification and
risk assessment should be carried out before any modification or introduction of new methods, materials, machinery or processes, in practice a result of risk assessment
obtained at a given moment is considered as an average
level of risk that was encountered over a longer period
of time.
Furthermore, it is common practice to carry out risk
assessment collectively, i.e., by addressing groups of workers who are exposed to the same or a similar set of harmful
agents. The risk assessment process is then carried out
once in relation to these groups. Therefore, the results indicate an average risk level that does not refer to individual
worker at a specific workstation, but is a resultant value,
generalised for relevant groups of workers or groups of
workstations.
However, with regard to emerging concepts of the
SWE, the previously described static models of OSH
risk management become insufficient. An increasing complexity of production technologies, and radical changes
in manufacturing and delivery of products to the market, resulting, among other things, from the concepts of
Industry 4.0 and Smart Factory, have brought about new
challenges for work processes. The customer, as well as
the market itself, expect more from manufacturers, namely
more connections, more adaptability and more responsiveness. Customers want more models and variants, specific to
their individual usage profile and requirements, so factories
must become more customer-centric, delivering products
that do more and better meet the diversified needs of individual clients. The factories must enable the manufacturer
to deliver products by radically shortening their production
cycles and by implementing dynamic processes ensuring a high degree of product variation and customisation.
As a result of such dynamically changing manufacturing
processes, the workplaces, together with their potential
hazards and the environment surrounding the worker, will
be subjected to frequent fluctuations imposed by hardly
predictable process variations. This situation will be radically different from the present one where static production
processes prevail, and a relatively accurate prediction of
hazards and risks in the working environment is possible.
Therefore, a possibility will open up for timely implementation of adequate protective and preventive measures.
Furthermore, implementation of the new technological
possibilities entails the growing number of jobs requiring
multitasking skills. Workers will no longer be able to perform their tasks routinely; instead, they will have to change
their place of work more frequently and undertake varied
and mostly unstructured tasks, depending on the needs of
the dynamically changing production process. The smart
technologies will significantly increase a level of mobility for workers, and the notion of a workstation will be
decoupled from the physical location of the worker.[100]
Real-time adjustment of different types of protective measures and protection levels that are suited to individual
workers’ needs will make it possible to ensure adequate
levels of safety and comfort at work without detriment to
the requirements of multi-tasking, mobility and dynamic
adaptability to changes in production processes, since those
needs may at any given time be significantly different, even
for workers who work in close locations.
In light of this discussion, a search for new methods
and organisational solutions within OSH management is
necessary to keep up with an increasing emergence of new,
and often yet unexplored, hazards in the working environment. In particular, there is a need for new approaches
that will be more adapted to increasing complexity of
production technologies, and to the pace of changes in
new working environments. It has been expected that the
implementation of AmI technologies into the workplace
will sufficiently address the mentioned challenges. But
to achieve this effect a significant change is needed in
the approach to OSH risk assessment and management as
compared with the one that is currently practised in enterprises. The fundamental two qualities of this paradigm shift
consist of:
• the assessment of occupational risks in a dynamically changing production environment, in a continuous and real-time manner. This approach is contrary to methods applied so far that assume risk
assessment to be carried out periodically. It is also
linked with a real-time adaptation of respective risk
controls to dynamic changes in the working environment. The goal is to anticipate hazards and immediately reduce the risk level and to maintain it at a
minimum, acceptable level;
• personalisation of occupational risk assessment
by conducting separate assessments for individual
workers who, at any given time, may be exposed
to various factors, and depending on the level of
exposure, could be protected against them in a diversified manner. This is contrary to the traditional
approach, which assumes collective risk assessment,
i.e., assessment for groups of workers that work
on similar workstations, and thus assumes application of risk controls adjusted to averaged needs of
such groups, but not to the individual needs of each
worker.
International Journal of Occupational Safety and Ergonomics (JOSE)
4.2.
Introducing context-awareness and context-based
reasoning systems for SWE
4.2.1. Basic notions and principles
Because of the growing inherent complexity of SEs and
the CPS architecture, which is reflected inter alia by a
large number of different types of physical objects functioning in these environments, as well as by the variety of their services and relations between these objects,
there is an increasing need for developing formal context models to facilitate context representation, context
sharing and semantic interoperability.[101,102] Contextawareness and context-based reasoning constitute basic
functionalities of ICT-based systems that are used in AmI
applications.[102–104] In particular, these features are
important in environments where frequent and dynamic
changes may occur and affect human users’ behaviour and
their interactions with other objects.
The first studies on context-based computing considered application of electronic badges that transmitted signals through a network of sensors to provide information
about the location of office workers.[105] The notion of
‘context-aware computing’ was used for the first time in
Schilit and Theimer’s study [106], and was referred to
as the ability of a mobile user’s application to discover
and react to changes in the user’s environment. Abowd
et al. [107, p. 304] defined context as ‘ . . . any information that can be used to characterize the situation of an
entity, where an entity can be a person, place, or physical
or computational object’.
Context-aware computing allows collecting and processing of data received from many sensors and networks in order to make the interpretation more easily and
meaningfully.[108] Literature points out several alternative methods of context modelling, e.g., key-value models, mark-up scheme models, graphic models, object-role
based models, ontology-based models and hybrid modelling schemes, but according to several sources the most
suitable for modelling context-awareness and reasoning in
SEs might be the ontology-based approach.[102,104]
In terms of philosophy, ontology is a branch of philosophical studies that deals with the nature of being, becoming, existence or reality. However, in the domain of computer sciences and engineering, the notion of ontology
is used commonly in a narrower sense, according to the
definition proposed initially by Gruber [109, p. 199] which
states that ‘An ontology is an explicit specification of
a conceptualization’. While according to Chandrasekaran
et al.,[110, p.20] ‘Ontologies are content theories about
the sorts of objects, properties of objects, and relations
between objects that are possible in a specified domain
of knowledge’. In the literature, three main categories
of ontologies are usually determined: (a) generic ontologies for capturing general, domain-independent knowledge; (b) domain ontologies, used for capturing knowledge
in the specific domains of science and technology; and (c)
9
application ontologies, which capture the knowledge necessary for a specific application.[111] Ontologies used for
the modelling context in SE applications usually belong
to the last category because they have to consider specific
environmental features, as well as a specific terminology
related to technologies, devices and functionality of smart
objects.
4.2.2. Examples of context-aware applications in SEs
Since the concept of context-aware computing is of a
generic nature, it may be implemented in almost every CPS
application aimed at providing services in various aspects
of life, business, science, etc. Review of the literature
reveals a large number of studies focused on applications
that are based on context awareness, and can be used for
different types of SE, disregarding the types of users and
the nature of services realised by these systems (see, e.g.,
[108,112–118]). Nevertheless, in the context of a discussion on the selection and adoption of appropriate methodologies for SWE development, particular attention should
be paid to methods and other achievements in thematically
neighbouring domains, such as smart healthcare, ambient assisted living (AAL) and smart homes (also referred
to as smart living). A reader may learn about example
applications of context-aware systems in these domains in
literature reviews published, e.g., in [119–123].
Recently, context-awareness functionality has also
started to be used in ICT applications developed for running multi-object CPSs, intended to ensure workers’ safety
in the SWE. Yet only few research studies have been carried out and published for this particular field. For example,
in the systematic inventory of more than 300 safety-related
ICT solutions, which can be potentially applied in the
SWE,[124] only few of them are based on context awareness. This result reflects a relatively low level of advancement of these techniques in the OSH field, compared
with the number of similar applications in other domains.
Selected examples of context-aware ICT solutions applied
in the OSH area are: (a) smart safety monitoring system based on a wireless communication network of smart
objects worn by workers on construction sites [125]; (b)
real-time automatic monitoring systems, ensuring safety in
harsh environments [126–128]; (c) a human–robot interaction system that ensures human safety by precisely tracking
the body of the operator and by activating relevant safety
strategies [129]; and (d) a simple safety system based on
GPS and smartphones for monitoring trains approaching
for railway track workers.[65]
4.2.3.
A need for a specific ontology for modelling
context in SWEs
From the presented state of the art it can be concluded that
we are now dealing with applications consisting of relatively small sets of few intelligent, but autonomous devices
10
D. Podgórski et al.
and systems, but not yet with the construction of a homogeneous SE, in which all smart objects connected with the
control of manufacturing processes (so-called Manufacturing Sphere), and all smart devices connected with ensuring workers’ safety (so-called Worker Sphere), would be
closely integrated across these spheres and would act collectively in the framework of homogeneous CPS to simultaneously achieve economic benefits and workers’ safety
and satisfaction. So far, no consistent conceptual framework has been proposed for context-aware management of
smart devices in both of the mentioned spheres to ensure
that they communicate effectively with each other. But
such an ontology-based framework is needed for developing reasoning engines and automated decision-making
applications to make them capable of understanding the
environment.[130]
The ontology models are often being elaborated by
means of specialised ontology mapping tools,[131] they
are usually drafted with the help of special tools [132,133]
and they are usually presented visually in the form of
graphs to facilitate understanding of relations between
individual levels and objects of the ontology concept.
There have been several attempts so far to develop
ontologies for systematisation and modelling of OSHrelated knowledge (see, e.g., [134–136]), but these
attempts have not been aimed at supporting elaboration of
algorithms for CPS functioning in the framework of the
SWE. On the other hand, the SE ontology models currently
proposed in the literature do not consider key concepts that
are specific to OSH management, such as hazard, environmental factor, occupational risk or risk control/prevention
action. Therefore, to model the context in the SWE one has
to exploit various concepts of ontology models that have
been proposed for applications in other types of SEs (see,
e.g., [130]).
4.2.4.
A need for applying probabilistic methods for
context reasoning and OSH risk assessment
As already mentioned in Section 4.1, an important functionality of CPS devoted to managing OSH in the SWE
is the capability of performing real-time and personalised
assessment of risks, taking into account all hazardous
factors appearing in a dynamically changing work environment. There are many risk assessment methods that have so
far been developed (e.g., see reviews in [137,138]). In general, risk assessment techniques can be divided into two
categories based on the deterministic and the probabilistic
(stochastic) approaches, but the use of a single method for
risk assessment will not ensure sufficiently reliable results.
Therefore, it is advisable to use mixed approaches based
on both deterministic and probabilistic methods, preferably
taking into account that probabilistic methods may have
an advantage over the deterministic ones because the former are more cost-effective and their results are easier to
communicate to decision-makers.[139,140]
The literature sources devoted to ICT applications in
SEs reveal that in the case of relatively simple CPS architectures, which consist of several sensors and actuators
(e.g., localisation tracking and warning system for workers
operating in dangerous zones), the methods for context reasoning and risk assessment are usually rule based and have
a deterministic nature. One may find a few examples of
such applications in [65,82,141,142]. However, in the case
of advanced CPSs consisting of many sensors monitoring
various parameters of users’ health and the environment,
and which are used for supporting management in complex scenarios of users’ activities and their relations with
SE objects, the use of probabilistic methods or the use of
methods based on fuzzy logic is needed.[143–145] This
approach is particularly necessary in SEs where a certain
level of uncertainty occurs, which is related, e.g., to the
recognition and prediction of user behaviour, inaccuracy
of sensors, missing information, imperfect observations
and inferring on the basis of imprecise and conflicting
data. In such cases, the practical use of such probabilistic methods as Bayesian networks (BNs), Hidden Markov
models (HMMs) and Dempster–Shafer theory (DST) of
evidence (also referred to as a theory of belief functions) is often advocated and indicated in the literature
(see [108,146,147]).
4.3. A model of CPS for the SWE
To summarise the challenges for the conceptual framework of OSH management within the SWE that have been
described and discussed in previous sections, a general
model of the CPS is proposed in Figure 2. This demonstrates basic concepts and functionalities of this system.
The proposed SWE conceptual model is composed of
two partly overlapping spheres: the Manufacturing Sphere
and the Worker Sphere. Both are covered by networks of
integrated smart objects that act collectively to ensure the
workers’ safety, comfort and satisfaction, without detriment to the highest possible productivity and economic
benefits. The basic modules of the CPS hardware layer are
as follows:
• a network of sensors which collect data from the
Worker Sphere, i.e., data on physical and chemical
factors of the working environment, physiological
parameters of workers, which make it possible to
draw conclusions about their health condition, the
current status of protective devices (e.g., detecting
end-of-service life of PPE), and data concerning
location of workers in relation to machinery and
other objects belonging to the SWE;
• a network of sensors which collect data from the
Manufacturing Sphere, i.e., data on current status
of the manufacturing processes, potential equipment malfunctions and failures, and the location
International Journal of Occupational Safety and Ergonomics (JOSE)
11
Figure 2. Model of a cyber-physical system applied for risk management in a smart working environment.
Note: PPE = personal protective equipment.
of mobile machinery and equipment in relation to
the operators;
• a network of activators, namely the controllers
of individual and collective protective equipment
(safety devices), aimed at preventing or reducing
emissions of harmful agents into the working environment, i.e., at active protection of potentially
exposed workers against these agents.
SMs hold a special position within the hardware of
the CPS model. They are able to sense specific environmental and/or physiological parameters and, at the same
time, react to these stimuli by changing their properties (see Section 3.1). Depending on a specific application, SMs can simultaneously perform sensory and activation functions, thus constituting a simple stand-alone
PPE solution, or they may be incorporated together with
other components into a complex ICT-based smart PPE
system.
As regards the software layer, the main components of
the CPS model include the following:
• context database that contains historical and current
data collected from all sensors assigned to various
smart objects, functioning in the Manufacturing and
Worker Spheres, as well as data reflecting the state of
preventive and protective measures controlling the
level of risk;
• reasoning engine, i.e., a module responsible for contextual data analysis, context-based reasoning with
regard to the current and possible future situations
(prediction), and for carrying out real-time assessment of risks for individual workers;
• risk control manager, which analyses resources
available for controlling the risks, selects appropriate preventive and protection measures given their
functionalities and potential effectiveness, as well as
activates these measures and monitors their usage.
12
D. Podgórski et al.
5. Discussion
In general, development and implementation of emerging technologies not only entails addressing a number of
conceptual and technological issues connected with ensuring their functionality, practical implementation, reliability, security, efficiency, the need to reduce operating costs,
etc., but also requires to take into account their potential
impact on target users and individuals indirectly exposed
to the influence of these technologies, and, in broader perspective, on the environment and the society.[148,149]
In the context of broadly understood sustainable development, the same considerations are also relevant in the
case of SMs, smart PPE and other OSH-related applications in the SWE field. Exploitation of these technologies
in the field of OSH is a relatively new phenomenon, hence
more research and innovation activities are still required
to refine and strengthen their potential for practical application, ensure users’ acceptability and achieve compliance
with societal and ethical requirements.
In the following sections, selected issues from the
aforementioned areas will be discussed. These issues may
be of an importance to further development, successful
implementation and wider spreading of the concept of a
dynamic and personalised OSH risk management within
the framework of the SWE.
5.1.
Further development of enabling technologies
As the literature review (Section 3) reveals, there has
already been significant technological progress in the field
of SMs, smart PPE and other OSH-related ICT applications. However, in many cases, the technology readiness
level that these solutions offer is still relatively low. In consequence, this prevents their mass introduction onto the
market, and use in practice. There is therefore a need for
further interdisciplinary research and innovation activities
in the field of enabling technologies that provide grounds
for the design of hardware and software components of
the CPS. This domain comprises of, among others, technologies resulting from SM engineering (e.g., temperatureresponsive polymers, dielectric elastomers, shape-memory
alloys and polymers, non-Newtonian fluids), sensor and
actuator technologies (e.g., based on fibre optics, thermoresistance, chemoresistance, photoelectrics, accelerometry), wireless communication, micro-electro-mechanical
systems (MEMS), nano-electronics and information technologies (e.g., multi-agent system modelling, contextaware and intention-aware computing, machine learning,
semantic technologies, artificial neural networks). Further
progress in these fields should lead, first of all, to significant miniaturisation of all CPS components, the improvement of their reliability, robustness, mechanical and thermal resistance, the improvement of sensor sensitivity and
selectivity, better resistance against EMF disturbances, as
well as ensuring energy efficiency, cost-effectiveness and
the reduction of an environmental impact.
5.2. Practical implementation of the CPS model
Before effective functioning of CPS dedicated to the SWE
can be put in place, a number of technological, organisational and implementation problems needs to be addressed.
As regards complex CPS that may serve a large number
of users and consists of many different sensors, actuators
and other smart objects, a serious challenge lies in the
acquisition and processing of raw data. They are needed
to fill up the context database with information concerning the objects, including the data on the users and their
location. From a technical point of view, the data acquisition methods can be divided into two main types: push and
pull. In the ‘pull’ method a special software component
makes requests from sensors periodically or instantly to
acquire data, while in the ‘push’ method sensors push data
to a software component dedicated to acquiring context
data periodically or instantly.[108] In both cases, different techniques and technological solutions are used for
the acquisition and pre-processing of contextual information which are determined by physical phenomena used
by the sensors, data formats and coding, WSN architecture, etc. For example, one may enumerate here collaborative WSNs,[150] data-mining frameworks,[151,152]
multisensory data fusion [153] and various infrastructures
of context-provisioning middleware.[118]
The next challenge in designing the CPS lies in providing good quality data collected from WSNs and stored in
the context database and their validation. In this regard one
may use, e.g., user-driven design and context-aware emulation to ensure user’s acceptance and to detect anomalous
events in SEs.[118,154,155] Increasingly, virtual reality
(VR) methods are used for modelling SE applications and
for usability verification of context models and inferring
algorithms in virtually simulated target use conditions.
Recent dynamic developments of VR technologies, and
decreasing costs of VR system components and developing the software, result in the creation of broader and more
inviting opportunities for SE modelling and usability testing. Some examples of these approaches are provided in
[156–160].
The need to provide a context-awareness functionality
in the SWE requires the proper creation and implementation of the reasoning layer of the CPS software. In this
regard, there are already many available methods and supporting tools. User situation rule engineering is a relatively
simple method based on the reasoning process, which consists of the inference of a user’s situation by means of rulebased reasoning techniques applied to the user profile and
context data (e.g., see [161]). Other methods draw on more
advanced techniques belonging to the domain of supervised machine learning, such as artificial neural networks,
genetic algorithms or support vector machines.[152,162–
166] In addition, practical aspects of the design of reasoning engines also include the application of probabilistic
methods, such as BNs, HMMs, and the DST, to which
reference was made earlier in Section 4.2.4.
International Journal of Occupational Safety and Ergonomics (JOSE)
Furthermore, the usability of the SWE can be enhanced
by ensuring self-adaptation of CPS to highly dynamic
environments where relying on fixed operational models would be inefficient. In such cases, dynamic context
management methods are used. They are based on feedback loops to enable governing the context database and
the reasoning engine in runtime. For example, a contextaware governance feedback loop consists of a context
monitor, which monitors specific attributes of the environment. The identified symptoms are then used by a
service-oriented architecture (SOA) governance controller
to adjust context-awareness governance objectives and
monitoring infrastructure accordingly.[167] In particular,
the context management infrastructure should maintain
context consistency by flexible reaction to the inclusion
of new sensors, and thus the emergence of new contextual
data. The system should also be resistant to any failure or
a shutdown of a single device, which should not lead to a
loss of control over the part of the environment, and to the
need to restart the whole system.[168]
5.3. Security issues and data protection
Taking into account the dynamic development of IoT and
CPS technologies, security threats and challenges are one
of the most important issues in this area, which have been
addressed in numerous research studies (e.g., [169–171]).
Information security in the context of computer technology
is defined as the preservation of confidentiality, integrity
and information availability.[172] Although principles of
ensuring security for ICT systems are already in place
and numerous standards and guidelines for information
security management have already been elaborated (e.g.,
series of ISO/IEC 27000 standards), they are typically
focused on traditional ICT, but not on CPS applications.
For example, in the context of preventing the security
threats of the CPS, the following system vulnerabilities
were identified: poor access control (lack of authentication), poor input validation, lack of robustness, implementation errors, limited interoperability, lack of preventative
safety, naive assumptions about security, proprietary solutions and safety lockouts.[173] Another important challenge is to ensure that CPS applied in smart manufacturing
systems are immune to cyber-attacks, which are difficult to
detect and prevent.[174,175]
The need to ensure adequate protection of data processed by CPS, particularly in the context of possible
cyber-attacks, is closely connected with privacy protection,
which is understood as a set of technical, legal and organisational procedures intended to protect personal data of SE
users in order to prevent and eliminate violations of their
rights to privacy. With regard to the protection of sensitive
data, which are processed in the SWE, a special attention should be paid to collecting and handling workers’
personal medical data. According to Ehrwein Nihan,[176]
medical data that are measured in the workplace belong to
13
workers, and there should be primacy of the privacy protection and workers’ health over productivity to gain users’
acceptance of AmI technologies. Furthermore, workers
should be provided with comprehensive and transparent
information about the data that will be collected, and control procedures for workers’ data handling and protection
against false data should be established.
Another specific problem concerns the increasing use
of user localisation technologies (e.g., GPS) and RFID
tags, especially in such SE applications where RFID tags
enable the acquisition and sharing of personal data. Such
systems can infer from sensed data not only the location,
but also how long the user stays at one place, whom a person met, where one might be going and other sensitive and
personal information.[177] Therefore, individuals should
always be informed of the presence of RFID tags and
readers, the purpose for which the data are collected and
processed, who the responsible controller is and whether
the data are stored and made available to other parties.[178]
However, privacy protection and system performance
have conflicting requirements and goals,[177] and autonomy of SWE users is inclined to conflict with safety
and health.[176] Therefore, it is particularly important to
investigate these issues to determine the adequate balance
between them. It is also necessary to develop and implement privacy procedures, including algorithms and protocols, to protect the sensed data against unwanted collection
and distribution of personal information. In this context,
a promising direction of research seems to be the development of privacy-friendly and anonymity-focused CPS
applications, examples of which constitute solutions proposed for smart homes [179] and for so-called anonymous
opportunistic sensing.[180]
5.4. Human factors and usability
Consideration of human factors and ergonomics aspects
in the design of SE applications, particularly the adjustment of their behaviour to take into account the functional and non-functional requirements of the users, is of
fundamental importance for the practical implementation
of these applications.[181,182] However, despite many
research studies in this field, this falls short of satisfactory
results.[3,183]
The central rule in the creation of ergonomic applications in this domain is striving to ensure usability by
following human-centred design principles. With regard
to CPS, the usability and usefulness factors to be considered in further development, design and application of CPS
should, first of all, take account of services that are integrated into their context of use, intuitive human–machine
interactions, flexibility regarding when they are used, efficient performance of the service and permanent service
accessibility and availability.[182]
As outlined in Section 4.2., context-awareness functionality plays an increasingly important role in the
14
D. Podgórski et al.
development of CPS applications intended to control complex SEs. In order to build such a system, it is necessary
to develop a model of a user context, which often entails
the need for specific ontologies that will allow describing
and understanding relations between various attributes and
functions of users and smart objects. Moreover, contextaware systems are usually very complex and the level of
variability is too high for designers to successfully predict and model human behaviour and system functioning in
advance. In this context, there is a need for research aimed
at comprehensive understanding of the complete spectrum
of types of human-in-the loop controls, development of
techniques to derive models of human behaviours and to
incorporate human behaviour models into the methodology of feedback control.[184] Meeting the aforementioned
challenges is particularly important when designing CPS
for SWE, where the main goal is to ensure safety for workers, because the unpredictable behaviour of such a system
may have severe consequences for the health and lives of
humans.
Another specific challenge in the area of human factors
in the design of SWEs is the development of new test methods and evaluation criteria for the smart PPE. In the EU, the
legal framework for mandatory assessment and CE certification of PPE is provided by Directive 89/686/EEC.[185]
European standards (EN) that are harmonised with this
Directive contain detailed assessment criteria and specify
test methods for all ‘traditional’ types of PPE. However,
because of the substantial technological progress which has
occurred in the last two decades, and a recent appearance
of a number of novel solutions of smart PPE, whose new
features and functions have not so far been foreseen in
EN standards, it is necessary to carry out a range of prenormative research in order to fill in those gaps. The main
topics to be addressed include, among others, ergonomics
and comfort of smart PPE, and compatibility of different system components, especially when ICT, electronic
modules and other advanced technologies are integrated in
PPE. The topics were determined in a mandate of the European Commission [186] that was launched to the European
Committee for Standardization (CEN), the European Committee for Electrotechnical Standardization (CENELEC)
and the European Telecommunications Standards Institute (ETSI) in order to explore emerging standardisation
needs in the field of smart protective textiles, clothing and
equipment.
6. Conclusions
A literature review included in this article shows how the
recent technological progress in emerging domains, such
as AmI, the IoT and CPS, has provoked numerous attempts
to apply ICT-based solutions in the area of OSH. A wide
range of SMs, smart PPE and wireless sensor systems
have already been developed with a view to improving
workers’ safety and health, thus laying a sound foundation
for the creation and implementation of the SWE concept.
Simultaneously, new challenges for work processes and
for OSH management have been brought about by an
increasing complexity of production technologies, as well
as by customer-centric approaches. They call for dynamic
processes in order to meet the requirements for a high
degree of product variation and customisation related, inter
alia, to the ideas of Industry 4.0 and Smart Factory. As a
result of dynamically changing manufacturing processes,
workplaces, together with their potential hazards and the
environment surrounding workers, will be subjected to frequent fluctuations that are imposed by hardly predictable
process variations. New approaches to OSH risk management are therefore needed to sufficiently address the
mentioned challenges.
In light of this discussion, a conceptual framework for
dynamic OSH risk management within the SWE has been
developed. The framework is based on a new paradigm
for dynamic and personalised OSH risk assessment and
management that consists of the continuous assessment
of risks in a real-time manner, and the capacity to assess
and monitor the risk level of each worker individually.
Application of the mentioned framework has been demonstrated on the background of the SWE concept and a
model of SWE-dedicated CPS. The SWE is understood
as the location of a workplace together with a complex of
surrounding conditions, in which workers perform workrelated tasks, and in which monitoring of environmental
and workers’ physiological parameters, as well as the
interaction between workers, the environment and smart
physical objects (e.g., machinery, working tools, safety
devices, PPE), is supported by respective AmI technologies. The SWE is viewed as being composed of two partly
overlapping spheres: the Manufacturing Sphere and the
Worker Sphere. These spheres are covered by networks
of integrated smart objects that should act collectively
to fulfil two equal and complementary objectives: (a) to
ensure workers’ safety and comfort; and (b) to maintain the
highest possible productivity and quality of manufacturing
processes.
Further advancements in the area of the IoT, CPS concepts and SWE-dedicated AmI technologies will result in
the widespread implementation of SMs, smart PPE and
other ICT solutions into the working environment, leading
finally to a state in which devices performing safety functions will be inseparably and invisibly incorporated into the
SWE infrastructure. These expected developments can be
illustratively described and promoted as a vision of ‘ubiquitous safety’,1 which, by analogy to the term ‘ubiquitous
computing’, can be defined as striving to incorporate SMs,
sensors, actuators and other ICT-based devices into any
object, machinery, tool, piece of clothing and equipment
that surrounds a worker and/or is worn or used by a worker.
The goal is to attain a level of embodiment of electronic
and computing devices into various smart objects of the
SWE, at which they become indistinguishable from these
International Journal of Occupational Safety and Ergonomics (JOSE)
objects, and are able to efficiently perform safety functions
in a user-friendly manner with respect to the requirements
of the users and the society.
However, to meet these expectations, further interdisciplinary research and innovation activities are needed.
These should aim to have the potential of the novel
approaches refined and strengthened, users’ acceptability ensured and compliance with societal requirements
achieved. The main focus should be given to the development of enabling technologies that provide grounds for
the design of robust, reliable and cost-efficient components
of the SWE infrastructure. Furthermore, the activities at
stake should concentrate on solving a number of practical
issues. They involve inter alia efficient and reliable acquisition of context data, ensuring CPS fault tolerance and
capacity for self-adaption to changing conditions. They
also include concerns with information security and data
protection, human factors and ethical aspects in the design
and implementation of SWE applications.
[4]
[5]
[6]
[7]
[8]
[9]
[10]
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This article is based on the results of several projects carried out
at national and international levels. The national projects were
carried out within the scope of the second and third stages of the
National Programme ‘Improvement of safety and working conditions’ partly supported in 2011–2013 and 2014–2016 – within
the scope of research and development – by the Ministry of Science and Higher Education/National Centre for Research and
Development and – within the scope of state services – by the
Ministry of Labour and Social Policy. The Central Institute for
Labour Protection – National Research Institute (CIOP-PIB) was
the Programme’s main co-ordinator. The international projects
(i-Protect and SAFeRA) that contributed most to developing the
concepts presented in this article have received funding from
the EU Seventh Framework Programme (FP7/2007–2013) under
grant agreements No. 291812 and 229275.
[11]
[12]
[13]
[14]
[15]
[16]
[17]
Note
1. The term ‘ubiquitous safety’ is already referred to in Amar
et al. [187], but in a narrow meaning, specifically with respect
to the automated anomaly detection techniques for rotary
machines.
References
[1] Aly S, Pelikán M, Vrana I. A generalized model for quantifying the impact of ambient intelligence on smart workplaces: applications in manufacturing. J Ambient Intell
Smart Environ. 2014;6:651–673.
[2] European Agency for Safety and Health at Work (EUOSHA). Priorities for occupational safety and health
research in Europe: 2013–2020. Bilbao: EU-OSHA; 2013.
[3] PEROSH. Sustainable workplaces of the future – European research challenges for occupational safety and
[18]
[19]
[20]
[21]
[22]
15
health. Brussels: PEROSH (Partnership for European
Research on Occupational Safety and Health); 2012.
Aarts E, Encarnação J. Into ambient intelligence. In: Aarts
E, Encarnação J, editors. True visions: the emergence of
ambient intelligence. Berlin: Springer; 2006. p. 1–16.
Augusto JC. Ambient intelligence: the confluence of ubiquitous/pervasive computing and artificial intelligence. In:
Schuster AJ, editor. Intelligent computing everywhere.
London: Springer; 2007. p. 213–234.
Cook DJ, Augusto JC, Jakkula VR. Ambient intelligence:
technologies, applications, and opportunities. Pervasive
Mob Comput. 2009;5:277–298. doi:10.1016/j.pmcj.2009.
04.001.
Nixon P, Lacey G, Dobson S. Managing interactions in
smart environments. Paper presented at: Managing interactions in smart environments. 1st International Workshop;
1999 Dec 13–14; Dublin, Ireland.
Governing Board of ECSEL JU. ECSEL multi annual
strategic plan 2016. Brussels: Governing Board of ECSEL
JU; 2015.
Weiser M. The computer for the 21st century. Sci Am.
1991;265:94–104. doi:10.1038/scientificamerican0991-94.
Krill P. IBM research envisions pervasive computing.
ComputerWorld [Internet]. North Sydney: IDG Communications; 2000 [cited 2016 Jul 5]. Available from: http://
www.computerworld.com.au/article/77594/ibm_research_
envisions_pervasive_computing/.
Satyanarayanan M. Pervasive computing: vision and challenges. IEEE Pers Commun. 2001;8:10–17. doi:10.1109/
98.943998.
Ashton K. That “Internet of Things” thing. RFiD Journal
[Internet]. 2009 Jun 22 [cited 2016 Jul 5]. Available from:
http://www.rfidjournal.com/articles/pdf?4986.
Miorandi D, Sicari S, De Pellegrini F, et al. Internet of
Things: vision, applications and research challenges. Ad
Hoc Netw. 2012;10:1497–1516. doi:10.1016/j.adhoc.2012.
02.016.
Atzori L, Iera A, Morabito G. The Internet of Things: a
survey. Comput Netw. 2010;54:2787–2805. doi:10.1016/
j.comnet.2010.05.010.
Middleton P, Kjeldsen P, Tully J. Forecast: The Internet of
Things, worldwide. Stamford (CT): Gartner; 2013.
Kagermann H, Wahlster W, Helbig J. Securing the future
of German manufacturing industry. Recommendations for
implementing the strategic initiative Industrie 4.0. Final
report of the Industrie 4.0 Working Group. Munich (Germany): Acatech; 2013.
Barthevan L. DLG-Expert report 5/2015. Industry 4.0 –
Summary report. Frankfurt a. M. (Germany): DLG, Competence Center Food Business; 2015.
Hermann M, Pentek T, Otto B. Design principles for Industrie 4.0 scenarios. Paper presented at: System Sciences
(HICSS). 49th Hawaii International Conference; 2016 Jan
5–8; Waikoloa, Hawaii, USA.
Akhras G. Smart materials and smart systems for the
future. Can Mil J. 2000;08:25–32.
Mondal S. Phase change materials for smart textiles –
an overview. Appl Therm Eng. 2008;28:1536–1550.
doi:10.1016/j.applthermaleng.2007.08.009.
Bartkowiak G, Dabrowska
˛
A, Marszałek A. Analysis
of thermoregulation properties of PCM garments on the
basis of ergonomic tests. Text Res J. 2013;83:148–159.
doi:10.1177/0040517512460299.
McCarthy LK, di Marzo M. The application of phase
change material in fire fighter protective clothing.
Fire Technol. 2012;48:841–864. doi:10.1007/s10694-0110248-3.
16
D. Podgórski et al.
[23] Gao C. Phase change materials (PCMs) for cooling or
warming in protective clothing. In: Wang F, Gao C, editors.
Protective clothing: managing thermal stress. Cambridge
(UK): Woodhead; 2014. p. 227–249.
[24] Horie K, Baron M, Fox RB, et al. Definitions of terms
relating to reactions of polymers and to functional polymeric materials (IUPAC Recommendations 2003). Pure
Appl Chem. 2004;76:889–906. doi:10.1351/pac20047604
0889.
[25] Bartkowiak G. Liquid sorption by nonwovens containing
superabsorbent fibres. Fibres Text East Eur. 2006;14:57–
61.
[26] Irzmańska E, Brochocka A. Influence of the physical and
chemical properties of composite insoles on the microclimate in protective footwear. Fibres Text East Eur.
2014;5:89–95.
[27] Paul R, editor. Functional finishes for textiles: improving
comfort, performance and protection. Cambridge (UK):
Woodhead; 2015.
[28] Li K, Zhen H, Niu L, et al. Full-solution processed flexible organic solar cells using low-cost printable copper electrodes. Adv Mat. 2014;26:7271–7278.
doi:10.1002/adma.201403494.
[29] Rocha JG, Goncalves LM, Rocha PF, et al. Energy harvesting from piezoelectric materials fully integrated in
footwear. IEEE Trans Ind Electron. 2010;57:813–819.
doi:10.1109/TIE.2009.2028360.
[30] Leonov V. Thermoelectric energy harvesting of human
body heat for wearable sensors. IEEE Sens J. 2013;13:
2284–2291. doi:10.1109/JSEN.2013.2252526.
[31] Mattila HR, editor. Intelligent textiles and clothing. Cambridge (UK): Woodhead in association with The Textile
Institute; 2010.
[32] Paul G, Gim E, Westerfeld D. Battery powered heating and
cooling suit. Paper presented at: Systems, Applications and
Technology. IEEE Conference; 2014 May 2; Farmingdale
(NY), USA.
[33] Demiryont H, Shannon K, Isidorsson J, et al. All solid
state electrochromic device for helmet-mounted displays.
Paper presented at: Head- and helmet-mounted displays
XIII: design and applications. SPIE Conference; 2008 Mar
17–18; Orlando (FL), USA.
[34] Tao XM. Introduction. In: Tao XM, editor. Wearable electronics and photonics. Cambridge (UK): Woodhead in
association with The Textile Institute; 2005. p. 1–12.
[35] Burchard B, Jung S, Ullsperger A, et al. Devices, software,
their applications and requirements for wearable electronics. Paper presented at: Consumer Electronics. International Conference; 2001 Jun 19–21; Los Angeles (CA),
USA.
[36] Marwedel P, editor. Embedded system design. Embedded
systems foundations of cyber-physical systems. New York
(NY): Springer; 2011.
[37] Liu H, Darabi H, Banerjee P, et al. Survey of wireless
indoor positioning techniques and systems. IEEE T Syst
Man Cy C. 2007;37:1067–1080. doi:10.1109/TSMCC.
2007.905750.
[38] Hennigs C, Hustedt M, Kaierle D, et al. Passive and active
protective clothing against high-power laser radiation.
Physics Procedia. 2013;41:291–301. doi:10.1016/j.phpro.
2013.03.081.
[39] Kirstein T, Troster G, Locher I, et al. Wearable assistants for mobile health monitoring. In: Van Langenhove
L, editor. Smart textiles for medicine and healthcare.
Materials, systems and applications. Cambridge (UK):
Woodhead in association with The Textile Institute; 2007.
p. 253–275.
[40] Mukhopadhyay SC. Wearable sensors for human activity
monitoring: a review. IEEE Sensors J. 2015;15:1321–
1330. doi:10.1109/JSEN.2014.2370945.
[41] Mason A, Wylie S, Korostynska O, et al. Flexible e-textile
sensors for real-time health monitoring at microwave
frequencies. Int J Smart Sensing Intell Syst. 2014;7:
31–47.
[42] Elenko E, Underwood L, Zohar D. Defining digital
medicine. Nat Biotechnol. 2015;33:456–461. doi:10.1038/
nbt.3222.
[43] Milici A, Amendola S, Bianco A, et al. Epidermal RFID
passive sensor for body temperature, measurements. Paper
presented at: RFID Technology and Application. IEEE
Conference; 2014 Sep 8–9; Tampere, Finland.
[44] Witt J, Krebber K, Demuth J, et al. Fiber optic heart rate
sensor for integration into personal protective equipment.
Paper presented at: Biophotonics. International Workshop;
2011 Jun 8–10; Parma, Italy.
[45] Taelman J, Adriaensen T, van der Horst C, et al. Textile integrated contactless EMG sensing for stress analysis.
Paper presented at: Engineering in Medicine and Biology
Society. 29th IEEE Annual International Conference; 2007
Aug 23–26; Lyon, France.
[46] Forsyth JB, Martin TL, Young-Corbett D, et al. Feasibility of intelligent monitoring of construction workers for
carbon monoxide poisoning. IEEE Trans Autom Sci Eng.
2012;9:505–515. doi:10.1109/TASE.2012.2197390.
[47] Pietrowski P. New PPE system development based on
integration of sensors, nanomaterials and ICT solutions
with protective clothing – i-Protect project approach. In:
Bartkowiak G, Frydrych I, Pawłowa M, editors. Innovations in clothing technology & measurement techniques.
Łódź: Lodz University of Technology; 2012. p. 174–
179.
[48] Witt J, Schukar M, Krebber K, et al. Personal protective
equipment with integrated POF sensors. Paper presented
at: Optical fibre sensors. 5th European workshop; 2013
May 20; Cracow, Poland.
[49] Voirin G. Working garment integrating sensor applications
developed within the PROeTEX project for firefighters.
In: Kinder-Kurlanda K, Nihan CE, editors. Ubiquitous
computing in the workplace. Cham: Springer; 2015. p.
25–33.
[50] Hertleer C, Odhiambo S, Van Langenhove L. Protective
clothing for firefighters and rescue workers. In: Chapman
R, editor. Smart textiles for protection. Cambridge (UK):
Woodhead; 2013. p. 338–363.
[51] Skrifvars M, Syrjala S, Myllari V, et al. The effect of
melt spinning process parameters on the spinnability of
polyetheretherketone. J Appl Polym Sci. 2012;126:1564–
1571. doi:10.1002/app.36930.
[52] Guo Z, Hagstrom B. Preparation of polypropylene/nanoclay
composite fibers. Polym Eng Sci. 2013;53:2035–2044.
[53] Niedermann R, Wyss E, Annaheim S, et al. Prediction of human core body temperature using non-invasive
measurement methods. Int J Biometeorol. 2014;58:7–15.
doi:10.1007/s00484-013-0687-2.
[54] Pulido Herrera E, Kaufmann H, Secue J, et al.
Improving data fusion in personal positioning systems
for outdoor environments. Inf Fusion. 2013;14:45–56.
doi:10.1016/j.inffus.2012.01.009.
[55] Mrugala D, Ziegler F, Kostelnik J, et al. Temperature sensor measurement system for firefighter gloves. Procedia
Eng. 2012;47:611–614. doi:10.1016/j.proeng.2012.09.221.
[56] Breckenfelder C, Mrugala D, An C, et al. A cognitive glove sensor network for fire fighters. In: LopezCozar R, Aghajan H, Augusto JC, et al., editors. Ambient
International Journal of Occupational Safety and Ergonomics (JOSE)
[57]
[58]
[59]
[60]
[61]
[62]
[63]
[64]
[65]
[66]
[67]
[68]
[69]
[70]
[71]
[72]
intelligence and smart environments. Washington (DC):
IOS Press; 2010. p. 158–169.
Schmidt A, Beringer J, Rupp M, et al. Sensor-based personal protective equipment “HORST” for forestry work
with power saws. Paper presented at: Future of protective clothing. Intelligent or not? 5th European Conference
on Protective Clothing and NOKOBETEF 10; 2012 May
29–31; Valencia, Spain.
Neves CAM, Manuel CRV, Pereira DASPA, et al. Inventors; Steelpro- Engenharia Industrial Lda, assignee. Wearable safety device and system for sawing, cutting and
milling machines. WO patent. 2,015,140,770. 2015 Dec
17.
Kumar LA, Vigneswaran C. Electronics in textiles and
clothing: design, products and applications. Boca Raton
(FL): CRC Press; 2016.
Bartkowiak G, Dabrowska
˛
A, Włodarczyk B. Construction
of a garment for an integrated liquid cooling system. Text
Res J. 2015;85:1809–1816. doi:10.1177/004051751557
6324.
Weder M, Hess M. Inventors; Empa Eidgenössische
Materialprüfungs- Und Forschungsanstalt, Unico Swiss
Text Gmbh, assignee. Cooling device. WO patent
2,011,131,718. 2011 Oct 27.
Power vest. Fraunhofer-Gesellschaft Press Release [Internet]. Munich: Fraunhofer-Gesellschaft; [cited 2016 Jul
5]. Available from: http://www.fraunhofer.de/en/press/
research-news/2015/March/power-vest.html.
Kurczewska A, Leśnikowski J. Variable-thermoinsulation
garments with a microprocessor temperature controller. Int
J Occup Saf Ergon. 2008;14:77–87. doi:10.1080/10803
548.2008.11076751.
Han H, Park H, Jeon E. User acceptance of a light-emitting
diode vest for police officer. Fash Text Res J. 2013;15:834–
840. doi:10.5805/SFTI.2013.15.5.834.
Ku J-H, Park D-K. Developing safety management systems for track workers using smart phone GPS. Int J Control Autom. 2013;6:137–148. doi:10.14257/ijca.2013.6.
5.13.
Aiteanu D, Hillers B, Graser A. A step forward in manual welding: demonstration of augmented reality helmet. Paper presented at: Mixed and Augmented Reality
(ISMAR’03). 2nd IEEE and ACM International Symposium; 2003 Oct 7–10; Tokyo, Japan.
Wassom B. Augmented reality law, privacy, and ethics:
law, society, and emerging AR technologies. Waltham
(MA): Syngress; 2014.
Plum R. The use of auto ID systems for data acquisition:
intelligent PPE. KANBrief. 2011;3:7–8.
Kelm A, Laußat L, Meins-Becker A, et al. Mobile passive radio frequency identification (RFID) portal for automated and rapid control of personal protective equipment
(PPE) on construction sites. Autom Constr. 2013;36:38–
52. doi:10.1016/j.autcon.2013.08.009.
Owczarek G, Kurczewska A, Gralewicz G. Application
of ICT to smart personal protective equipment for safety
management in the working environment. Paper presented
at: Working on safety. 7th International Conference; 2014
Sep 30–Oct 03; Glasgow, UK.
Barro-Torres S, Fernandez-Carames TM, Perez-Iglesias
HJ, et al. Real-time personal protective equipment
monitoring system. Comput Commun. 2012;36:42–50.
doi:10.1016/j.comcom.2012.01.005.
Reiffenrath M, Hoerr M, Gries T, et al. Smart protective
clothing for law enforcement personnel. Mater Sci. Text
Cloth Technol. 2014;9:64–68.
17
[73] Koncar V, editor. Smart textiles and their applications.
Vol. 178, Woodhead series in textiles. Cambridge (UK):
Woodhead in association with The Textile Institute; 2016.
[74] Cho CG, editor. Smart clothing, technology and applications. Boca Raton (FL): CRC Press; 2010.
[75] Stoppa M, Chiolerio A. Wearable electronics and smart
textiles: a critical review. Sensors. 2014;14:11957–11992.
doi:10.3390/s140711957.
[76] Zurawski R, editor. Embedded systems handbook, second
edition: networked embedded systems. Boca Raton (FL):
CRC Press; 2009.
[77] Jara AJ, Ladid L, Skarmeta A. The Internet of everything
through IPv6: an analysis of challenges, solutions and
opportunities. J Wirel Mob Netw Ubiquit Comput Depend
Appl (JoWUA). 2013;4:97–118.
[78] Minoli D. Building the Internet of Things with IPv6 and
MIPv6: the evolving world of M2M communications.
Hoboken (NJ): Wiley; 2013.
[79] Sammarco JJ, Paddock R, Fries EF, et al. A technology
review of smart sensors with wireless networks for applications in hazardous work environments. Pittsburgh (PA):
National Institute for Occupational Safety and Health;
2007.
[80] Nasr E, Shehab T, Vlad A. Tracking systems in construction: applications and comparisons. Paper presented
at: 49th ASC Annual International Conference; 2013 Apr
9–13; San Luis Obispo (CA), USA.
[81] Schiffbauer WH, Mowrey GL. An environmentally robust
proximity warning system for hazardous areas. Paper presented at: Emerging Technologies. ISA Conference; 2001
Sep 10–13; Houston (TX), USA.
[82] Teizer J, Allread BS, Fullerton CE. Autonomous proactive real-time construction worker and equipment operator proximity safety alert system. Automat Constr.
2010;19:630–640. doi:10.1016/j.autcon.2010.02.009.
[83] Petracca M, Bocchino S, Azzarà A, et al. WSN and RFID
integration in the IoT scenario: an advanced safety system
for industrial plants. J Commun Softw Syst. 2013;9:104–
112.
[84] Hu S, Tang C, Yu R, et al. Intelligent coal mine monitoring
system based on the Internet of Things. Paper presented
at: Consumer Electronics, Communications and Networks
(CECNet). 3rd International Conference; 2013 Nov 20–22;
Xianning, China.
[85] Bhattacharjee S, Roy P, Ghosh S, et al. Wireless sensor network-based fire detection, alarming, monitoring and
prevention system for bord-and-pillar coal mines. J Syst
Soft. 2012;85:571–581. doi:10.1016/j.jss.2011.09.015.
[86] Seminatore AA, Ghelardoni L, Ceccarelli A, et al.
ALARP – a railway automatic track warning system
based on distributed personal mobile terminals. Procedia
Soc Behav Sci. 2012;48:2081–2090. doi:10.1016/j.sbspro.
2012.06.1181.
[87] Lian KY, Hsiao SJ, Sung WT. Mobile monitoring and
embedded control system for factory environment. Sensors. 2013;13:17379–17413. doi:10.3390/s131217379.
[88] Fathallah HE, Lecuire V, Rondeau E, et al. Development of an IoT-based system for real time occupational exposure monitoring. Paper presented at: Systems and Networks Communications (ICSNC 2015). 10th
International Conference; 2015 Nov 15–20; Barcelona,
Spain.
[89] Rampa V, Vicentini F, Savazzi S, et al. Safe human—robot
cooperation through sensor-less radio localization. Paper
presented at: Industrial Informatics. 12th IEEE International Conference; 2014 Jul 27–30; Porto Alegre, Brazil.
18
D. Podgórski et al.
[90] Rybski PE, Anderson-Sprecher P, Huber D, et al. Sensor fusion for human safety in industrial workcells. Paper
presented at: Intelligent Robots and Systems. IEEE/RSJ
International Conference; 2012 Oct 7–12; Vilamoura, Portugal.
[91] Lázaro O, Moyano A, Uriarte M, et al. Integrated and personalised risk management in the sensing enterprise. In:
Banaitiene N, editors. Risk management – current issues
and challenges. Rijeka: InTech; 2012. p. 285–312.
[92] Gisbert JR, Palau C, Uriarte M, et al. Integrated system
for control and monitoring industrial wireless networks for
labor risk prevention. J Netw Comput Appl. 2014;39:233–
252. doi:10.1016/j.jnca.2013.07.014.
[93] Aneziris ON, Topali E, Papazoglou IA. Occupational
risk of building construction. Reliab Eng Syst Saf.
2012;105:36–46. doi:10.1016/j.ress.2011.11.003.
[94] Aneziris ON, Papazoglou IA, Psinias A. Occupational
risk for wind farms. In: Steenbergen RDJM, van Gelder
PHAJM, Miraglia S, et al., editors. Safety, reliability
and risk analysis: beyond the horizon. Leiden: CRC
Press/Balkema; 2014. p. 1489–1496.
[95] Ale BJM, Baksteen H, Bellamy LJ, et al. Quantifying
occupational risk: the development of an occupational risk
model. Saf Sci. 2008;46:176–185. doi:10.1016/j.ssci.2007.
02.001.
[96] National Institute for Public Health and the Environment (RIVM). The quantification of occupational risk. The
development of a risk assessment model and software.
Bilthoven: RIVM; 2008.
[97] Frick K. European Union’s legal standard on risk assessment. In: Karwowski W, editor. Handbook of standards
and guidelines in ergonomics and human factors. Mahwah
(NY): Erlbaum; 2006. p. 471–492.
[98] International Labour Organization (ILO). Guidelines on
occupational safety and health management systems:
ILO-OSH 2001. Geneva: International Labour Office;
2001.
[99] OSHwiki contributors. Prevention and control strategies [Internet]. OSHwiki [cited 2015 July 15]. Available
from: http://oshwiki.eu/index.php?title = Prevention_and_
control_strategies&oldid = 245257.
[100] Zuehlke D. SmartFactory – from vision to reality in factory
technologies. Paper presented at: International Federation
of Automatic Control. 17th World Congress; 2008 Jul 6–
11; Seoul, Korea.
[101] Wang XH, Zhang DQ, Gu T, et al. Ontology based context modeling and reasoning using OWL. Paper presented
at: Pervasive Computing and Communications Workshops. Second IEEE Annual Conference; 2004 Mar 14–17;
Orlando (FL), USA.
[102] Bettini C, Brdiczka O, Henricksen K, et al. A survey
of context modelling and reasoning techniques. Pervasive
Mob Comput. 2010;6:161–180. doi:10.1016/j.pmcj.2009.
06.002.
[103] Hong J, Suh E, Kim S-J. Context aware systems: a
literature review and classification. Expert Syst Appl.
2009;36:8509–8522. doi:10.1016/j.eswa.2008.10.071.
[104] Castillejo E, Almeida A, López-de-Ipiña D, et al.
Modeling users, context and devices for ambient
assisted living environments. Sensors. 2014;14:5354–
5391. doi:10.3390/s140305354.
[105] Want R, Hopper A, Falcao V, et al. The active badge
location system. ACM Trans Inf Syst. 1992;10:91–102.
doi:10.1145/128756.128759.
[106] Schilit B, Theimer M. Disseminating active map information to mobile hosts. IEEE Netw. 1994;8:22–32. doi:10.
1109/65.313011.
[107] Abowd GD, Dey AK, Brown PJ, et al. Towards a better understanding of context and context-awareness. In:
Gellersen H-W, editor. Handheld and ubiquitous computing. Berlin: Springer; 1999. p. 304–307.
[108] Perera C, Zaslavsky A, Christen P, et al. Context
aware computing for the Internet of Things: a survey.
IEEE Commun Surv Tut. 2014;16:414–454. doi:10.1109/
SURV.2013.042313.00197.
[109] Gruber TR. A translation approach to portable ontology
specifications. Knowl Acquis. 1993;5:199–220. doi:10.
1006/knac.1993.1008.
[110] Chandrasekaran B, Josephson JR, Benjamins VR. What
are ontologies, and why do we need them? IEEE Intell
Syst. 1999;14:20–26. doi:10.1109/5254.747902.
[111] Ye J, Coyle L, Dobson S. Ontology-based models in pervasive computing systems. Knowl Eng Rev. 2007;22:315–
347.
[112] Ejigu D, Scuturici M, Brunie L. An ontology-based
approach to context modeling and reasoning in pervasive
computing. Paper presented at: Pervasive Computing and
Communications Workshops. 5th Annual IEEE International Conference; 2007 Mar 19–23; White Plains (NY),
USA.
[113] Saleemi MM, Rodríguez ND, Lilius J, et al. A framework for context-aware applications for smart spaces. In:
Balandin S, Koucheryavy Y, Hu H, editors. Smart spaces
and next generation wired/wireless networking. Berlin:
Springer; 2011. p. 14–25.
[114] Almeida A, López-de-Ipiña D. A distributed reasoning engine ecosystem for semantic context-management
in smart environments. Sensors. 2012;12:10208–10227.
doi:10.3390/s120810208.
[115] McAvoy L, Chen L, Donnelly M. An ontology based context management system for smart environments. Paper
presented at: Mobile Ubiquitous Computing, Systems,
Services and Technologies. 6th International Conference;
2012 Sep 23–28; Barcelona, Spain.
[116] Corno F, Sanaullah M. Modeling and formal verification of
smart environments. Secur Commun. 2014;7:1582–1598.
[117] Conti M, Das SK, Bisdikian C, et al. Looking ahead in pervasive computing: challenges and opportunities in the era
of cyber-physical convergence. Pervasive Mob Comput.
2012;8:2–21. doi:10.1016/j.pmcj.2011.10.001.
[118] Knappmeyer M, Kiani SL, Reetz E, et al. Survey of context provisioning middleware. IEEE Commun Surv Tut.
2013;15:1492–1519.
doi:10.1109/SURV.2013.010413.
00207.
[119] Bricon-Souf N, Newman CR. Context awareness in
health care: a review. Int J Med Inform. 2007;76:2–12.
doi:10.1016/j.ijmedinf.2006.01.003.
[120] Amiribesheli M, Benmansour A, Bouchachia A. A review
of smart homes in healthcare. J Ambient Intell Humaniz
Comput. 2015;6:495–517. doi:10.1007/s12652-0150270-2.
[121] Chahuara P, Portet F, Vacher M. Context aware decision system in a smart home: knowledge representation
and decision making using uncertain contextual information. Paper presented at: Acquisition, Representation and
Reasoning with Contextualized Knowledge (ARCOE-12).
4th International Workshop; 2012 Aug 28; Montpellier,
France.
[122] Wongpatikaseree K, Ikeda M, Buranarach M, et al. Activity Recognition using context-aware infrastructure ontology in smart home domain. Paper presented at: Knowledge, Information and Creativity Support Systems. 7th
International Conference; 2012 Nov 8–10; Melbourne,
Australia.
International Journal of Occupational Safety and Ergonomics (JOSE)
[123] Solaimani S, Keijzer-Broers W, Bouwman H. What we
do – and don’t – know about the smart home: an analysis of the smart home literature. Indoor Built Environ.
2013;24:1–14.
[124] Gralewicz G, Owczarek G. An inventory of selected electronic, textronic, mechatronic and ICT-based solutions for
safety-related applications in smart working environments
[Internet]. Warszawa: CIOP-PIB; [cited 2016 Jul 5]. Available from: https://www.ciop.pl/CIOPPortalWAR/file/754
56/An_inventory_of_selected_ITC_solutions_CIOP-PIB_
2015.pdf.
[125] Yang JM, Park JY, Im SY, et al. Context-awareness smart
safety monitoring system using sensor network. In: Kim
TH, Adeli H, Grosky WI, et al., editors. Multimedia, computer graphics and broadcasting. Berlin: Springer; 2011. p.
270–277.
[126] Mayton B, Dublon G, Palacios S, et al. TRUSS: Tracking risk with ubiquitous smart sensing. Paper presented
at: Sensors. 11th IEEE International Conference; 2012 Oct
28–31; Taipei, Taiwan.
[127] Yang H, Chew DAS, Wu W, et al. Design and implementation of an identification system in construction site
safety for proactive accident prevention. Accid Anal Prev.
2012;48:193–203. doi:10.1016/j.aap.2011.06.017.
[128] Arslan M, Zainab R, Kiani AK, et al. Real-time environmental monitoring, visualization and notification system for construction H&S management. ITcon. 2014;19:
72–91.
[129] Corrales JA, García Gómez GJ, Torres F, et al. Cooperative tasks between humans and robots in industrial
environments. Int J Adv Robot Syst. 2012;9:1–10.
[130] Wemlinger Z, Holder LB. The COSE ontology: bringing
the semantic web to smart environments. In: Abdulrazak
B, Giroux S, Bouchard B, et al., editors. Toward useful
services for elderly and people with disabilities. Berlin:
Springer; 2011. p. 205–209.
[131] Choi N, Song I-Y, Han H. A survey on ontology mapping.
SIGMOD Rec. 2006;35:34–41. doi:10.1145/1168092.116
8097.
[132] Sivakumar R, Arivoli PV. Ontology visualization Protégé tools – a review. Int J Adv Inf Commun Technol.
2011;1:1–11.
[133] Dudáš M, Zamazal O, Svátek V. Roadmapping and navigating in the ontology visualization landscape. In: Janowicz K, Schlobach S, Lambrix P, et al., editors. Knowledge engineering and knowledge management. Berlin:
Springer; 2014. p. 137–152.
[134] Galatescu A, Alexandru A, Zaharia C, et al. Ontologybased modeling and inference for occupational risk prevention. Advances in Semantic Processing. 4th International Conference; 2010 Oct 25–30; Florence, Italy.
[135] Gangolells M, Casals M. Un enfoque basado en ontología
para la gestión integrada del medio ambiente y de la seguridad y la salud en obra. [An ontology-based approach
for on-site integrated environmental and health and
safety management]. Revista Ingeniería de Construcción.
2012;27:103–127. doi:10.4067/S0718-50732012000300
001.
[136] Vigneron J, Guarnieri F, Rallo JM. The contribution of
ontologies to the creation of knowledge bases for the
management of legal compliance in occupational health
and safety. In: Steenbergen RDJM, van Gelder PHAJM,
Miraglia S, et al., editors. Safety, reliability and risk analysis: beyond the horizon. Boca Raton (FL): CRC Press;
2013. p. 1519–1524.
19
[137] Marhavilas PK, Koulouriotis D, Gemeni V. Risk analysis and assessment methodologies in the work sites: on
a review, classification and comparative study of the scientific literature of the period 2000–2009. J Loss Prev
Process Ind. 2011;24:477–523. doi:10.1016/j.jlp.2011.03.
004.
[138] Wang D, Dai F, Ning X. Risk assessment of workrelated musculoskeletal disorders in construction: stateof-the-art review. J Constr Eng Manage. 2015;141.
doi:10.1061/(ASCE)CO.1943-7862.0000979.
[139] Kirchsteiger C. On the use of probabilistic and deterministic methods in risk analysis. J Loss Prev Process Ind.
1999;12:399–419. doi:10.1016/S0950-4230(99)00012-1.
[140] Marhavilas PK, Koulouriotis DE. Developing a new alternative risk assessment framework in the work sites by
including a stochastic and a deterministic process: a case
study for the Greek public electric power provider. Saf Sci.
2012;50:448–462. doi:10.1016/j.ssci.2011.10.006.
[141] Giretti A, Carbonari A, Naticchia B, et al. Design and
first development of an automated real-time safety management system for construction sites. J Civ Eng Manag.
2009;15:325–336. doi:10.3846/1392-3730.2009.15.325336.
[142] Fugini M, Teimourikia M. Risks in smart environments
and adaptive access controls. Presented at: Innovative
Computing Technology (INTECH). 4th International Conference; 2014 Aug 13–15; Luton, UK.
[143] Ranganathan A, Al-Muhtadi J, Campbell RH. Reasoning about uncertain contexts in pervasive computing
environments. IEEE Pervasive Comput. 2004;3:62–70.
doi:10.1109/MPRV.2004.1316821.
[144] Jayaraman PP, Zaslavsky A, Delsing J. On-the-fly situation composition within smart spaces. In: Balandin S,
Moltchanov D, Koucheryavy Y, editors. Smart spaces
and next generation wired/wireless networking. Berlin:
Springer; 2009. p. 52–65.
[145] Singla G, Cook DJ, Schmitter-Edgecombe M. Tracking
activities in complex settings using smart environment
technologies. Int J Biosci Psychiatr Technol. 2009;1:25–
35.
[146] Ye J, Dobson S, McKeever S. Situation identification techniques in pervasive computing: a review. Pervasive Mob
Comput. 2012;8:36–66. doi:10.1016/j.pmcj.2011.01.004.
[147] Tolstikov A, Hong X, Biswas J, et al. Comparison of
fusion methods based on DST and DBN in human activity
recognition. JCTA. 2011;9:18–27.
[148] Floridi L. A look into the future impact of ICT on our lives.
Inform Soc. 2007;23:59–64. doi:10.1080/0197224060105
9094.
[149] Weatherbee TG. Counterproductive use of technology at
work: information & communications technologies and
cyberdeviancy. HRMR. 2010;20:35–44.
[150] Bal M, Shen W, Ghenniwa H. Collaborative signal and
information processing in wireless sensor networks: a
review. Paper presented at: Systems, Man, and Cybernetics. IEEE International Conference; 2009 Oct 11–14; San
Antonio (TX), USA.
[151] Chen C, Das B, Cook D. A data mining framework for
activity recognition in smart environments. Paper presented at: Intelligent Environments. 6th International Conference; 2010 Jul 19–21; Kuala Lumpur, Malaysia.
[152] Banaee H, Ahmed MU, Loutfi A. Data mining for wearable sensors in health monitoring systems: a review of
recent trends and challenges. Sensors. 2013;13:17472–
17500. doi:10.3390/s131217472.
20
D. Podgórski et al.
[153] Khaleghi B, Khamis A, Karray FO, et al. Multisensor
data fusion: a review of the state-of-the-art. Inf Fusion.
2013;14:28–44. doi:10.1016/j.inffus.2011.08.001.
[154] Nam CS, Konomi S. Usability Evaluation of QueryLens:
implications for context-aware information sharing using
RFID. Paper presented at: Human–Computer Interaction. IASTED International Conference; 2005 Nov 14–16;
Phoenix (AZ), USA.
[155] Ongenae F, Duysburgh P, Verstraete M, et al. User-driven
design of a context-aware application: an ambient intelligent nurse call system. Paper presented at: Pervasive
computing technologies for healthcare. 6th International
Conference; 2012 May 21–24; San Diego (CA), USA.
[156] O’Neill E, Lewis D, McGlinn K, et al. Rapid usercentred evaluation for context-aware systems. In: Doherty
G, Blandford A, editors. Interactive systems. Design,
specification, and verification. Berlin: Springer; 2007. p.
220–233.
[157] McGlinn K, O’Neill E, Gibney A, et al. SimCon: a tool
to support rapid evaluation of smart building application
design using context simulation and virtual reality. J USC.
2010;16:1992–2018.
[158] Lertlakkhanakul J, Choi J. Virtual place framework for
user-centered smart home applications. In: Al-Qutayri
MA, editor. Smart home systems. Rijeka: InTech; 2010.
p. 177–194.
[159] Propp S, Forbrig P. ViSE – a virtual smart environment for
usability evaluation. In: Bernhaupt R, Forbrig P, Gulliksen
J, et al., editors. Human–Centred Software Engineering.
Berlin: Springer; 2010. p. 38–45.
[160] Seo DW, Kim H, Kim JS, et al. Hybrid reality-based user
experience and evaluation of a context-aware smart home.
Comput Ind. 2016;76:11–23. doi:10.1016/j.compind.2015.
11.003.
[161] Goix LW, Valla M, Cerami L, et al. Situation inference for
mobile users: a rule based approach. Paper presented at:
Mobile Data Management. International Conference; 2007
May 1; Mannheim, Germany.
[162] Stenudd S. Using machine learning in the adaptive control
of a smart environment. Espoo: VTT; 2010.
[163] Lin YS, Chen RC, Lin YC. An indoor location identification system based on neural network and genetic
algorithm. Paper presented at: Awareness Science and
Technology (iCAST). 3rd International Conference; 2011
Nov 24–26; Abuja, Nigeria.
[164] Ray AK, Leng G, McGinnity TM, et al. Development
of cognitive capabilities for smart home using a selforganizing fuzzy neural network. Paper presented at:
Robot Control. 10th IFAC Symposium; 2012 Sep 5–7;
Dubrovnik, Croatia.
[165] Witte H, Rathgeb C, Busch C. Context-aware mobile biometric authentication based on support vector machines.
Paper presented at: Emerging Security Technologies. 4th
International Conference; 2013 Sep 9–11; Cambridge, UK.
[166] Kabir MH, Hoque MR, Seo H, et al. Development of a
smart home context-aware application: a machine learning based approach. Int J Smart Home. 2015;9:217–226.
doi:10.14257/ijsh.2015.9.1.23.
[167] Villegas NM, Müller HA. Context-driven adaptive monitoring for supporting SOA governance. Paper presented
at: Maintenance and Evolution of Service-Oriented Systems. 4th International Workshop; 2010 Sep 17; Timişoara,
Romania.
[168] Euzenat J, Pierson J, Ramparany F. Dynamic context
management for pervasive applications. Knowl Eng Rev.
2008;23:21–49. doi:10.1017/S0269888907001269.
[169] Banerjee A, Venkatasubramanian KK, Mukherjee T,
et al. Ensuring safety, security, and sustainability
[170]
[171]
[172]
[173]
[174]
[175]
[176]
[177]
[178]
[179]
[180]
[181]
[182]
[183]
[184]
[185]
[186]
of mission-critical cyber-physical systems. Proc IEEE.
2012;100:283–299. doi:10.1109/JPROC.2011.2165689.
Jing Q, Vasilakos AV, Wan J, et al. Security of the Internet of Things: perspectives and challenges. Wirel Netw.
2014;20:2481–2501. doi:10.1007/s11276-014-0761-7.
Abomhara M, Køien GM. Security and privacy in the
Internet of Things: current status and open issues. Paper
presented at: Privacy and Security in Mobile Systems.
International Conference; 2014 May 11–14; Aalborg,
Denmark.
International Organization for Standardization, International Electrotechnical Commission (IEC). Information
technology – security techniques – information security management systems – overview and vocabulary.
Geneva: International Organization for Standardization;
2014. Standard No. ISO/IEC 27000:2014.
Templeton SJ. Security aspects of cyber-physical device
safety in assistive environments. Paper presented at: Pervasive Technologies Related to Assistive Environments. 4th
International Conference; 2011 May 25–27; Crete, Greece.
Baum F, Bulthuis W, editors. White paper: managing security, safety and privacy in smart factories. Munich: Munich
Network and TÜV SÜD; 2015.
Wells LJ, Camelio JA, Williams CB, et al. Cyber-physical
security challenges in manufacturing systems. MFGLET.
2014;2:74–77.
Ehrwein Nihan CE. Ubiquitous computing in the workplace: ethical issues identified by the interdisciplinary
IWE and HRM Research Group. In: Kinder-Kurlanda K,
Ehrwein Nihan C, editors. Ubiquitous computing in the
workplace. Cham: Springer; 2015. p. 75–93.
De Hert P, Gutwirth S, Moscibroda A, et al. Legal safeguards for privacy and data protection in ambient intelligence. Pers and Ubiquitous Comput. 2009;13:435–444.
doi:10.1007/s00779-008-0211-6.
Armac I, Panchenko A, Pettau M, et al. Privacy-friendly
smart environments. Paper presented at: Next Generation Mobile Applications, Services and Technologies. 3rd
International Conference; 2009 Sep 16–18; Cardiff, UK.
Shin M, Cornelius C, Peebles D, et al. AnonySense: a system for anonymous opportunistic sensing. Pervasive Mob
Comput. 2011;7:16–30. doi:10.1016/j.pmcj.2010.04.001.
Stephanidis C. Human factors in ambient intelligence environments. In: Salvendy G, editor. Handbook of human
factors and ergonomics. Hoboken (NJ): Wiley; 2012. p.
1354–1373.
Geisberger E, Broy M, editors. Living in a networked
world. Integrated research agenda cyber-physical systems
(agendaCPS) (acatech STUDY). Munich: Herbert Utz;
2014.
Bibri SE. The human face of ambient intelligence: cognitive, emotional, affective, behavioral and conversational
aspects. Paris: Atlantis Press; 2015.
Stankovic JA. Research directions for the Internet of
Things. IEEE Internet Things J. 2014;1:3–9. doi:10.1109/
JIOT.2014.2312291.
Directive 89/686/EEC of the European Parliament and the
Council of 21 December 1989 on the approximation of the
laws of the Member States relating to personal protective
equipment. OJ. 1989;L399:18–38.
CEN-CLC BT WG 8. Programming mandate M/509:
protective textiles and personal protective clothing and
equipment; Final report. Brussels: CEN-CLC BT WG 8;
2014.
Amar M, Gondal I, Wilson C. Unitary anomaly detection for ubiquitous safety in machine health monitoring.
Paper presented at: Neural Information Processing. 19th
International Conference; 2012 Nov 12–15; Doha, Quatar.