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Sensor Networks to Monitor Elderly

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Title: Sensor Networks to Monitor Elderly


1
Sensor Networks to Monitor Elderly
Yusuf Albayram Computer Science
Engineering University of Connecticut,
Storrs yusuf.albayram_at_uconn.edu
2
Introduction
  • 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

3
Problems
  • With the increase of elderly people population
  • Rising Health Care Costs
  • More investment is needed for elderly care
  • Many elderly people choose to stay at home
  • e.g., Due to privacy/dignity issues.
  • 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
  • Decrease in the working population will cause a
    shortage of skilled caregivers.

4
State of the art applications
  • 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

5
What services can assisted living systems offer?
  • 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

6
Overview
  • Introduction Motivation
  • 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
  • Challenges

7
(1.1) Activities of Daily Living Monitoring
  • Monitoring the patients 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.

8
(1.2) Activities of Daily Living Monitoring
  • Variety of multi-modal and unobtrusive wireless
    sensors seamlessly integrated into
    ambient-intelligence 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
9
(2) Location Tracking
  • 25 of people over 60 suffer from Alzheimers
    and Dementia
  • Seniors with Dementia or Alzheimers can easily
    become confused or lost.
  • Monitoring location of a person suffering
    dementia or Alzheimers 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

10
(2.1) Location Tracking
  • (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
11
(2.2) Location Tracking
  • (2) RFID-based indoor location tracking
  • GPS does not work in indoor
  • Real-time monitoring of elderly peoples
    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
12
(2.3) Location Tracking
  • 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

13
(3) Medication Intake Monitoring
  • Taking medications is one of the most important
    activities in an elders daily life
  • Elders taking on average of about 5.7
    prescription medicines and 4 nonprescription
    drugs each day 15
  • Medication intake monitoring is essential
  • 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

14
(3.1) Medication Intake Monitoring
  • 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
15
(3.2) Medication Intake Monitoring
  • 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

RFID system includes antenna and RFID reader
Monitoring the activity of taking medications
using computer vision-based method
16
(4) Medical Status Monitoring
  • 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 providers end,
  • the medical personnel and supervising physicians
    can have instant access to
  • real-time physiological measurements
  • the medical history of several monitored patients

17
(4.1) Medical Status Monitoring
The health monitoring network structure 16
18
(5) Fall and Movement Detection
  • 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

19
(5.1) Fall and Movement Detection
  • (1) Wearable device based methods
  • Using accelerometers and gyroscopes to analyze
    changes in a bodys 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
20
(5.2) Fall and Movement Detection
  • (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

21
(5.3) Fall and Movement Detection
  • (3) Ambient based methods
  • Rely on pressure sensors, acoustic sensors or
    even passive infrared motion sensors, which are
    usually implemented around caretakers houses
  • Once the fall event was detected, an alert
    call/email was immediately sent.

22
(5.4) Fall and Movement Detection
  • 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.

23
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

24
References
  • 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 for monitoring fall detection of
    elderly in home environments." Computer and
    Information 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 reduce the false alarm rate."
    Engineering in Medicine and Biology Society,
    2008. EMBS 2008. 30th Annual International
    Conference of the IEEE. IEEE, 2008.
  • 6 Huang, Kung-Ta, et al. "An intelligent RFID
    system for improving elderly daily life
    independent in indoor environment." Smart homes
    and health telematics. Springer Berlin
    Heidelberg, 2008. 1-8.
  • 7 Ferreira, João. "Behavioral Analytics for
    Medical Decision Support Supporting dementia
    diagnosis through outlier detection." (2012).
  • 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
    ensure the safety of the elderly in smart home
    environments." Personal and ubiquitous computing
    17.8 (2013) 1699-1707.
  • 10 Hasanuzzaman, Faiz M., et al. "Monitoring
    activity of taking medicine by incorporating RFID
    and video analysis." Network Modeling Analysis in
    Health Informatics and Bioinformatics 2.2 (2013)
    61-70.

25
References-2
  • 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-deriv
    ed 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/406
    3195431780411/sf_falls.ppt
  • 16 Pantelopoulos, Alexandros, and Nikolaos G.
    Bourbakis. "Prognosisa wearable
    health-monitoring system for people at risk
    Methodology and modeling." Information Technology
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    613-621.
  • 17 Lu, Ching-Hu, and Li-Chen Fu. "Robust
    location-aware activity recognition using
    wireless sensor network in an attentive home."
    Automation Science and Engineering, IEEE
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