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Copyright © 2004
IEEE-USA Geriatric Care Working Group – June 2004
Howard D. WactlarCarnegie Mellon University
Pittsburgh, USA
CareMediaVideo and Sensor Analysis for Geriatric Care
Copyright © 2004
Goal: Automated Behavior Analysis in the Nursing Home
We’re interested in automating detection of behavioral & psychological symptoms of dementia (BPSD).
• Monitoring and maintaining the quality of life
• With Western Psychiatric Institute & Clinic (WPIC-UPMC, U.of Pgh)
Ultimately, we seek to make automated, quantitative measurements to:
• Explore relationship of BPSD to environments in which they occur
• Evaluate symptoms longitudinally
• Determine the frequency of BPSD
• Develop a patient profile of responses of BPSD to pharmacological and non-pharmacological interventions
• >>>> Enable earlier intervention to sustain quality of life
Falling
Social InteractionIdentification
Kathy Jones
Aggression
Emotiontime
Circadian Analysis
Behavioral Analysis & Summarization
Information Access
Skilled Nursing Facility
Information Extraction & Identification
Activity & Environmental MonitoringLocation Biometrics SensorVideo
00011101
Pri
va
cy
Te
ch
no
log
y &
Po
lic
y
Doctor
Clinical
Researchers
Caregiver
Pharmaceutical
Companies
Hospitals
Public Health
Location Gesture
Time
Event People
SoundsMovement
Database
…
Environment
CareMedia Overview
Copyright © 2004
Copyright © 2004
Applications in the Nursing Home
• Clinical/Research
• Tracking patient behavior and incidents in long-term care facilities• e.g., disruptive vocalizations, falls• recording patient mobility and activity levels
• Correlating with time of day, location and environmental factors• Observing effects of drugs on individuals and groups
• Patient• Cognitive assist - reminding, alerting and summoning help
• Staff training• Analysis of video records of incidents used for training
• Management• Monitoring and documenting compliance
Copyright © 2004
CareMedia: What are the observables?
• Who? • Identify people across
cameras, days.
• What are they doing?• Wandering around
• Working on tasks
• Looking for things
• Eating, sleeping in public • How well did they do it?
• Quantify normal performance
• Detect/report anomalies
Query and Summary Reporting Daily patient summary distance traveled sleep behaviors triggers affect Location summaries use of space environmental triggers
Searchable video index by patient by time by event by location by behavior
Visual Processing Color Edge detection Motion detection Object detection Head/hands/torso det. Event detection Face ID Gait ID
Audio Processing Event detection Voice detection Voice ID Speech to text
Integration and Indexing Person ID Fall detector Behavior detection Interaction detection Interaction classification Aggressive Social
Hallway
rawvideo
& audio
rawvideo
& audio
Information Extraction and Reporting
Copyright © 2004
Copyright © 2004
Current research in activity recognition can loosely be grouped into a couple of categories:
1. kinematic/dynamic(Wren, Ma, Blake, Black, Freeman)
2. non-parametric, statistical(Davis, Liu, Lee, Yang, Cutler, Shi)
Interpreting Behavior
Copyright © 2004
Coarse Motion Measurement
Applying mean-shift analysis:
target detection
red indicates target
Informedia Digital Libraries
Copyright © 2004
Fine Motion with Directions
Applying optical flow analysis:
Copyright © 2004
Summarization Visualization
Large hr. of video
Measure Normal Activity, Detect What’s Not
Find pattern
Few important events
Copyright © 2004
Problem: Privacy Protection in Public Places
• Block the persons that are reluctant to be captured in the video
• ¼ of nursing home residents deny disclosure of their images
• Real-time automatic people tracking framework
• Detect foreground information; adapt for real-time background
• Multi-target, multi-assignment blob matching
• Apply mean shift algorithm to separate merged persons
Copyright © 2004
Problem: Monitoring in Private Spaces
• “Observe” and monitor activity without storing video• Maintain only feature vectors; classify in real-time
• Record event type, time of day, duration
• Detect changes in daily pattern of activity
• Example: Monitor bathroom/mirror activities• What: brushing teeth, combing hair, washing hands, washing
face
• How: small camera behind center of mirror, mono microphone, embedded computing
• Create summary
• how long, how often, chart by day
Copyright © 2004
Copyright © 2004
Future Opportunities
• Upgrade to hi-resolution cameras for fine motor detection• Measure tremors, facial expressions
• Instrument with distributed sensors for precision• Force sensors in chairs, beds, carpeting
• RFID in clothing, utensils
• Conduct large-scale testbeds for validation• Comprehensive instrumentation in multiple homes
• Move through lesser levels of care to expand market• From constrained skilled care environments to less structured
assisted and independent living
• >>>> Enable earlier detection and intervention
• Delaying nursing home entry by 1 month saves $1.2B/year
Copyright © 2004
Thank You
Questions?
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