SEeS@W: Internet of Persons Meets
Internet of Things for Safety at Work
Alessio Antonini
University of Torino
antonini@unito.it
Carlo Salaroglio
University of Torino
salaroglio@unito.it
Guido Boella
University of Torino
boella@unito.it
Luigi Sanasi
University of Torino
sanasi@unito.it
Federica Cena
University of Torino
cena@di.unito.it
Claudio Schifanella
University of Torino
claudio.schifanella@unito.it
Alessia Calafiore
University of Torino
alessia.calafiore@unito.it
Agata Marta Soccini
University of Torino
soccini@di.unito.it
Ilaria Lombardi
University of Torino
lombardi@di.unito.it
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Abstract
SEeS@w faces the problem of safety in working
environments in an innovative way, putting together
objects and people, and the virtual and real word. We
aimed at designing and developing a demonstrative
prototype of an innovative ICT solution to monitor,
evaluate and manage risks in a complex cooperative
working environment. Data about risks are provided by
workers themselves using interactive maps, according
to the Internet of Persons paradigm. Maps are also fed
by other data collected by networks of ambient and
wearable sensors, connected to the Internet, according
to the Internet of Things paradigm. Maps display and
brings all these data together, and can be therefore
used by workers as a powerful instrument to coordinate
people, manage risk issues, and improve safety at
work. Thanks to the Living Lab methodology, we
brought together the technical and human aspects of
the project, testing the solution in terms of effectively,
acceptability, usability and ergonomics.
Author Keywords
Internet of Persons, Interactive Maps, Crowdsourcing,
Internet of Things; Socio-technical systems; Safety at
Work; Risk Detection; Wearable devices.
ACM Classification Keywords
H.5.m. Information interfaces and presentation (e.g.,
HCI): Miscellaneous
Introduction
Preventing risks and keeping workplaces safe and
healthy increase the quality of work as well as the
competitiveness of companies. Workers’ health has a
positive, direct and quantifiable influence on
productivity, while inefficient safety systems lead to
unnecessary costs. Accidents and injuries in
workspaces have a huge direct economic impact on
companies, and also lead to a leak of motivation of the
workers. An efficient way to manage risks is therefore
essential, and should be based on increasing users’
participation and awareness. Users should be involved
in spotting, mapping and sharing information about
1
Figure 1: mappa su applicazione
mobile (orientamento verticale)
The project has been founded by POR-FESR - Axes I, Productive
innovation, Action Line I.1.3
risks in their working environments.
Starting from this consideration, the SEeS@w (Sensing
Safety at Work) project [1]1 faces workplace safety
issues with a socio-technical approach, giving users an
active role in risk monitoring and management. The
main goal of the project is to design and develop a
demonstrative novel solution to monitor and manage
risks, based on data collected both by users (Internet
of Persons), and by environmental sensors (Internet of
Things). First, users become sensors of information
about risk in their working environment, according to
the Internet of Persons paradigm, exploiting interactive
maps. Second, such information is integrated with
environmental data collected via Internet of Things
technologies. Several previous studies underlined the
benefits of the IoT paradigm in developing working
places as safety oriented systems [2], especially for
managing hazards and accidents in confined places [3].
Conversely, crowdsourcing is mainly used to collect
information users voluntarily share related to
geographical data (e.g., FixMyStreet [4] or
LoveCleanStreets [5]). The novelty of SEeS@w
approach is thus: i) to apply the IoP paradigm to the
problem of safety at work, and ii) to put together IoP
and IoT. Such approach improves also the coordination
among workers, crucial in risk management. In fact,
using a map to convey information creates an
immediate and intuitive comprehension of the problem.
In particular, maps provide a synoptic view of the
relevant aspects (alarms, instruments, procedures,
risks, messages, sensors, chemical substances), give
an idea of the situation at first sight, and let workers
Figure 2: mappa su applicazione
Web
actively collaborate with safety experts by adding
further risks or possible solutions to the maps, and thus
keeping alive the interest of the community. Moreover,
using maps we dramatically transform a classic
interaction scheme in a way more efficient and novel
paradigm. Instead of supporting a peer-to-peer
interaction that retrieves information from spread
sources, we purpose the use of maps as a hub to
gather data and users.
We merged the human and technological aspects of the
project, and were able to validate both, using Living
Lab [6] methodology in a real setting. We found in the
public health system a perfect and challenging test bed,
because of the complexity of the organization, the wide
differentiation of the tasks and the working hours, the
strict privacy policy. During the evaluation we
demonstrated the validity of the idea of merging IoT
and IoP for improving the managing of risks in high
complexity working places.
Architecture of the solution
The system (Fig.1) we developed in SEeS@W project is
fed by data coming from different sources. First of all,
information is gathered by users, both in a passive way
(through a wristwatch that recognizes anomalous
situations, such as falls and suspect immobility) and in
an active way (through messages that users post to an
interactive map application).
Information is also generated by sensors monitoring
the ambient conditions: environmental sensors, which
detect humidity, temperature and light, sensors
some analyses. Fists, it creates a semantic repository of
legal norms. Then, it provides useful correlation, such
as the correlation between indoor - on one side - and
Figure 3: marker su applicazione
Web
Figura 1: logical architecture of SEeS@W system
detecting reagents cabinet conditions (tilt, fall, liquid
overflow and percolation, cabinet doors
opening/closing, cabined air-forced ventilation, RFIDs
monitoring reagents containers pick and drop), iii)
chemical sensors, which monitor airborne substances
(formaldehyde, toluene, ethanol). Such data can be
sent directly to an Open Data Platform, Yucca Smart
data Platform [7] or, in case they need early
elaboration and/or aggregation, to an intermediate IoT
Platform.
Exploiting the acquired data, the system performs
outdoor - on the other side - temperature and humidity
percentage. This is particularly relevant since it exploits
open data in the Yucca platform. Moreover, an
algorithm correlates a person’s heart rate,
morphometric parameters, anagraphic and gender
features with their ventilation, and thus with airborne
pollutants absorption.
The system finally provides workers with integrated
information that can increase their awareness about
risks and can support them solving problems. The core
of the solution is the interactive maps application
(available both on the Web and as a mobile app) that
offers workers a synoptic view of the safety conditions,
informs them about possible risks and enables
discussion among involved people. Those maps show
markers pinpointing messages posted by users, alarms
related to sensors, messages about dangerous
situations, lab instruments, reagents cabinets and work
processes potentially dangerous for workers. Details
about such processes are available on the NormAgeos
[8], a platform that analyzes risks arising from
behaviors that are not compliant with procedures and
legal norms. Another feedback means is a solution
alerting the worker about possible dangerous situations
by combining augmented reality and wearable devices
(wristband, collar, or armband): when an alarm occurs,
the wearable device vibrates and a smart projector
lights the source of the risk. Finally, it is possible to
visualize historical data both on some dashboards
available on the Web and on an Android mobile app.
Experiment and discussion
“Open innovation environments, real life situations in
which final users are involved in the creation processes
of new services, products or social infrastructures”[1]
are the bases of the Living Lab methodology we used to
face the complexity of the socio-technical challenges.
We experimented our system at the Toxicology Lab of
the CTO and other analyses laboratories at “Cittá della
Salute e della Scienza”, the Pathological Anatomy lab at
Molinette Hospital. We implemented our solution in the
mentioned environments, and collected several sets of
data and user opinions in order to evaluate the solution
in terms of effectiveness, acceptability, usability and
ergonomics. According to the experiment results,
SEeS@w is a modular, effective, non-intrusive solution
to face safety issues in workplaces. The system,
especially the interactive maps application, can be
extended to several working environments that could
be deeply wider or dramatically different from the ones
we tested. As future work we plan to test the approach
in a longitudinal study, to evaluate the effects on
workers’ awareness and behavior, as well as in a totally
different working context.
References
[1] http://seesaw.di.unito.it
[2] Yang, L., Yang, S.H., Plotnick, L. 2013, “How the
internet of things technology enhances emergency
response operations”, Technological Forecasting
and Social Change, vol. 80, no. 9, pp. 1854–1867
[3] Botti, L., Duraccio, V., Gnoni, M.G., Mora, C. ,
2015. "A framework for preventing and managing
risk in confinated space with Iot technologies",
ESREL 2015- European Safety and Reliability Conf.,
Zurich, pp. 7-10
[4] https://www.fixmystreet.com
[5] www.lovecleanstreets.com
[6] J. Hess and C. Ogonowski, 2010. “Steps Toward a
Living Lab for Socialmedia Concept Evaluation and
Continuous user-Involvement,” in Proc. 8th Int.
Conference on Interactive TV&Video, 171–174.
New York, pp. 171-174.
[7] http://www.smartdatanet.it/presentation.html
[8] http://menslegis.augeos.it/