CareMedia

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CareMediaVideo and Sensor Analysis for
Geriatric Care
IEEE-USA Geriatric Care Working Group June 2004
Howard D. Wactlar Carnegie Mellon
University Pittsburgh, USA
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CareMediaVideo and Sensor Analysis for
Geriatric Care
Informedia Digital Libraries

• Howard Wactlar
• Chris Atkeson, Ashok Bharucha,M.D., Mike
  Christel,
• Alex Hauptmann, Dorbin Ng, Scott Stevens
• Carnegie Mellon University
• Pittsburgh, PA, USA
• April 2004

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Goal Automated Behavior Analysis in the Nursing
Home

• Were 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
• gtgtgtgt Enable earlier intervention to sustain
  quality of life

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CareMedia Overview
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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

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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

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What is Presently Measured by Humans

• The Pittsburgh Agitation Scale
• Aberrant Vocalizations
• (repetitious requests or complaints, non-verbal
  vocalizations, i.e. moaning)
• Motor Agitation
• (pacing, wandering, rocking in chair)
• Aggressiveness
• (vocal threats, threatening gestures)
• Resisting Care
• (pushing away to avoid tasks)

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Information Extraction and Reporting
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
Integration and Indexing Person ID Fall
detector Behavior detection Interaction
detection Interaction classification
Aggressive Social
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Interpreting Behavior

• 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)

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Coarse Motion Measurement
Informedia Digital Libraries

• Applying mean-shift analysis

target detection
red indicates target
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Fine Motion with Directions
Applying optical flow analysis
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Measure Normal Activity, Detect Whats Not
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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

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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

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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
• gtgtgtgt Enable earlier detection and intervention
• Delaying nursing home entry by 1 month saves
  1.2B/year

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Thank You Questions?