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.

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(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

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(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
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(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
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(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

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(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

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(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
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(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

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(4.1) Medical Status Monitoring
The health monitoring network structure 16
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(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

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(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
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(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

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(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.

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(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.

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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

• 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.

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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
  in Biomedicine, IEEE Transactions on 14.3 (2010)
  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
  Transactions on 6.4 (2009) 598-609.