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Sensor Networks to Monitor Elderly
Yusuf Albayram
Computer Science & Engineering
University of Connecticut, Storrs
yusuf.albayram@uconn.edu
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Introduction
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The proportion of elderly in the world is
demonstrating a remarkable increase every year.
 By the year 2050, 1 in 5 person in the world will
be age 60 or older,
1.6 million people in the aging population live in
facilities
Typical residents need assistance with 2 activities of
daily living
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Problems
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With the increase of elderly people population:
 Rising Health Care Costs
 More investment is needed for elderly care
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Many elderly people choose to stay at home
 e.g., Due to privacy/dignity issues.
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A majority of older adults are challenged by
chronic and acute illnesses and/or injuries.
 80% of older Americans have one or more chronic
diseases.

The growing insufficiency of traditional family
care
 i.e., decreased care by relatives
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Decrease in the working population will cause a
shortage of skilled caregivers.
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State of the art applications
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Advances in sensor technology, object localization,
wireless communications technologies can
 enable elderly people to regain their capability of
independent living
 make possible unobtrusive supervision of basic
needs of frail elderly and thereby replicate services
of on-site health care providers
Assisted Living Technologies are expected to
contribute significantly
 improving the quality of life of elders
 reducing costs by avoiding premature
institutionalization
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What services can assisted living systems offer?
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Alarms/notifications and triggers
Queries
Reminders
Detect anomalies and deviations
Recognize specific behaviors and assist with task
completion
Keep the person active and connected to the social
environment
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Overview
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Introduction & Motivation
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Sensor Networks to Monitor Elderly
 (1) Activities of Daily Living Monitoring,
 (2) Location Tracking,
 (3) Medication Intake Monitoring,
 (4) Medical Status Monitoring,
 (5) Fall and Movement Detection
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Challenges
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(1.1) Activities of Daily Living Monitoring
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Monitoring the patient’s activities of daily living
(ADLs) is essential to
 Detects anomalies and prompts them,
 Assist the independent living of older adults
 The diagnosis of diseases and health problems
Several projects have investigated the use of pervasive
sensors to provide a ‘smart’ environment for the
observation of (ADL)
 The use of heterogeneous sensors, including
 Wearable sensors (Body Sensor Network (BSN))
– Designed to collect biomedical, physiological and activity data
 Ambient sensors (Ambient Sensor Network (ASN))
– Designed to collect data around the region where the ADL takes
place.
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(1.2) Activities of Daily Living Monitoring
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Variety of multi-modal and unobtrusive wireless
sensors seamlessly integrated into ambientintelligence compliant objects (AICOs) to achieve
activity recognition
[17] Overview of assisted living populated with a variety of wireless multimodal sensors
to collect data for various ADLs
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(2) Location Tracking
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25% of people over 60+ suffer from Alzheimer’s and
Dementia
Seniors with Dementia or Alzheimer’s can easily
become confused or lost.
Monitoring location of a person suffering dementia or
Alzheimer’s can help
 Detect signs of disorientation or wandering.
 The health professional to reach a diagnosis of a
type of dementia.
Several methods for location tracking have been
proposed:
 (1) GPSs based outdoor location tracking
 (2) RFID-based indoor location tracking
 IR, ultrasound
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(2.1) Location Tracking
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(1) GPSs based outdoor location tracking
 GPS-enabled devices include an SOS button and
once pressed , connect with their family member or
caregiver.
GPS Tracker Bracelets
Wearable AGPS terminal
Smart Phone with GPS
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(2.2) Location Tracking
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(2) RFID-based indoor location tracking
GPS does not work in indoor
Real-time monitoring of elderly people’s whereabouts
 The movement of the elderly person wearing an
RFID tag is sensed by the RFID readers installed
in the building
The RFID-based location sensing system
in smart home environments
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(2.3) Location Tracking
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Critique for location tracking systems
 Privacy is one of the major issue
 Too battery-hungry and battery drain quickly
(e.g., smart phones)
 Devices must be lightweight, small, and
comfortable to wear and use
 Elders often have no idea using computers,
smartphones and other technological tools
 their interaction with them must be simple
 And limited to a minimum
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(3) Medication Intake Monitoring
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Taking medications is one of the most important
activities in an elder’s daily life
 Elders taking on average of about 5.7 prescription
medicines and 4 nonprescription drugs each day [15]
Medication intake monitoring is essential
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Medication noncompliance is common in elderly and
chronically ill especially when cognitive disabilities are
encountered [13].
The existing methods/systems often utilize following sensor
technologies for medication intake monitoring :
 RFID
 Computer vision
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(3.1) Medication Intake Monitoring
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Integrating both sensor network and RFID technologies
 HF RFID tags to identify when and which bottle is removed
or replaced by the patient
 The weight scale monitors the amount medicine on the scale
 The patient wearing an Ultra High Frequency (UHF) RFID
tag is determined in the vicinity and alert the patient to take
the necessary medicines.
Medicine Monitor System Prototype
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(3.2) Medication Intake Monitoring
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Incorporating RFID and video analysis [10]
 RFID tags applied on medicine bottles located in a
medicine cabinet and RFID readers detect if any
of these bottles are taken away
 A video camera monitoring the activity of taking
medicine by integrating face and mouth detection
Monitoring the activity of taking medications
using computer vision-based method
RFID system includes antenna
and RFID reader
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(4) Medical Status Monitoring
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Health monitoring devices are primary responsible for
 Collecting physiological data from the patient
 (e.g., ECG, heart rate, blood pressure)
Transmitting them securely to a remote site for
further evaluation
At the health provider’s end,
 the medical personnel and supervising physicians
can have instant access to
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 real-time physiological measurements
 the medical history of several monitored patients
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(4.1) Medical Status Monitoring
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The health monitoring
network structure [16]
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(5) Fall and Movement Detection
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Fall Events very common situation in elderly people
 30% of the older persons fall at least once a year
 Fall responsible of 70% of accidental death in
persons aged 75+
There are primarily 3 types of fall detection methods
for elderly
 (1) Wearable device based methods
 (2) Vision based methods
 (3) Ambient based methods
Once the fall event was detected, an alert email is
immediately sent to the caregiver
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(5.1) Fall and Movement Detection
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(1) Wearable device based methods
 Using accelerometers and gyroscopes to analyze
changes in a body’s position to detect falls.
the sensor nodes are attached
on the chest (Node A) and thigh
(Node B)
A tri-axial accelerometer for monitoring acceleration
and a tri-axial gyroscope for monitoring angular velocity [14]
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(5.2) Fall and Movement Detection
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(2) Vision based methods
 Detect Fall from a video sequence by:
 Applying background subtraction to extract the
foreground human body and post processing to improve
the result [2,3]
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(5.3) Fall and Movement Detection
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(3) Ambient based methods
 Rely on pressure sensors, acoustic sensors or even
passive infrared motion sensors, which are usually
implemented around caretakers’ houses
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Once the fall event was detected, an alert call/email
was immediately sent.
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(5.4) Fall and Movement Detection
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Critique for automatic fall detection,
 (+) Video based methods are usually more accurate
 (-) Video based methods raise privacy concerns
 (+) Acoustics based methods are very susceptible
to ambient noise
 (-) Video-based and acoustic-based methods are
costly due to pre-installation
 (-) Wearable based methods operate as long as the
person wears the sensors
 (+) With the improvements in smart phone tech
(built-in sensors e.g., accelerometer, gyroscope),
Smart phones are ideal for developing an app that
can automatically detect falls and provide a
warning mechanism.
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Challenges of Sensor Networks solutions for monitoring
elderly
 Hardware level challenges
 Unobtrusiveness
 Sensitivity and calibration
 Energy
 Data acquisition efficiency
 Security
 Privacy
 User-friendliness
 Ease of deployment and scalability
 Mobility
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References
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[1] Wang, J., et al. "An enhanced fall detection system for elderly person monitoring using consumer
home networks." Consumer Electronics, IEEE Transactions on 60.1 (2014): 23-29.
[2] Yu, Miao, et al. "A posture recognition-based fall detection system for monitoring an elderly
person in a smart home environment." Information Technology in Biomedicine, IEEE Transactions
on 16.6 (2012): 1274-1286.
[3] Foroughi, Homa, Baharak Shakeri Aski, and Hamidreza Pourreza. "Intelligent video surveillance
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Technology, 2008. ICCIT 2008. 11th International Conference on. IEEE, 2008.
[4] Yavuz, Gokhan, et al. "A smartphone based fall detector with online location support."
International Workshop on Sensing for App Phones; Zurich, Switzerland. 2010.
[5] Popescu, Mihail, et al. "An acoustic fall detector system that uses sound height information to
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Annual International Conference of the IEEE. IEEE, 2008.
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[7] Ferreira, João. "Behavioral Analytics for Medical Decision Support: Supporting dementia
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[8] Wong, AK-S., et al. "An AGPS-based elderly tracking system." Ubiquitous and Future Networks,
2009. ICUFN 2009. First International Conference on. IEEE, 2009.
[9] Kim, Soo-Cheol, Young-Sik Jeong, and Sang-Oh Park. "RFID-based indoor location tracking to
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[10] Hasanuzzaman, Faiz M., et al. "Monitoring activity of taking medicine by incorporating RFID
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(2013): 61-70.
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References-2
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[11] Pang, Zhibo, Qiang Chen, and Lirong Zheng. "A pervasive and preventive healthcare solution for
medication noncompliance and daily monitoring." Applied Sciences in Biomedical and
Communication Technologies, 2009. ISABEL 2009. 2nd International Symposium on. IEEE, 2009.
[12] Ho, Loc, et al. "A prototype on RFID and sensor networks for elder healthcare: progress report."
Proceedings of the 2005 ACM SIGCOMM workshop on Experimental approaches to wireless
network design and analysis. ACM, 2005.
[13] Alemdar, Hande, and Cem Ersoy. "Wireless sensor networks for healthcare: A survey."
Computer Networks 54.15 (2010): 2688-2710.
[14] Li, Qiang, et al. "Accurate, fast fall detection using gyroscopes and accelerometer-derived
posture information." Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth
International Workshop on. IEEE, 2009.
[15 ] Johnston C (2001) Falls in the Elderly, UCSF Division of Geriatrics Primary Care Lecture
Series. http://s3.amazonaws.com/engrademyfiles/4063195431780411/sf_falls.ppt
[16] Pantelopoulos, Alexandros, and Nikolaos G. Bourbakis. "Prognosis—a wearable healthmonitoring system for people at risk: Methodology and modeling." Information Technology in
Biomedicine, IEEE Transactions on 14.3 (2010): 613-621.
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