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Copyright © 2004 IEEE-USA Geriatric Care Working Group – June 2004 Howard D. Wactlar Carnegie Mellon University Pittsburgh, USA CareMedia Video and Sensor.

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Presentation on theme: "Copyright © 2004 IEEE-USA Geriatric Care Working Group – June 2004 Howard D. Wactlar Carnegie Mellon University Pittsburgh, USA CareMedia Video and Sensor."— Presentation transcript:

1 Copyright © 2004 IEEE-USA Geriatric Care Working Group – June 2004 Howard D. Wactlar Carnegie Mellon University Pittsburgh, USA CareMedia Video and Sensor Analysis for Geriatric Care

2 Howard Wactlar Chris Atkeson, Ashok Bharucha,M.D., Mike Christel, Alex Hauptmann, Dorbin Ng, Scott Stevens Carnegie Mellon University Pittsburgh, PA, USA April 2004 Informedia Digital Libraries Copyright © 2004

3 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 >>>> Enable earlier intervention to sustain quality of life

4 Falling Social Interaction Identification Kathy Jones Aggression Emotion time Circadian Analysis Behavioral Analysis & Summarization Information Access Skilled Nursing Facility Information Extraction & Identification Activity & Environmental Monitoring LocationBiometricsSensor Video Privacy Technology & Policy Doctor Clinical Researchers Caregiver Pharmaceutic al Companies Hospitals Public Health Location Gesture Time Event People Sounds Movement Database … Environment CareMedia Overview Copyright © 2004

5 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

6 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

7 Copyright © 2004 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)

8 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 raw video & audio raw video & audio Information Extraction and Reporting Copyright © 2004


10 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

11 Copyright © 2004 Coarse Motion Measurement Applying mean-shift analysis: target detection red indicates target Informedia Digital Libraries

12 Copyright © 2004 Fine Motion with Directions Applying optical flow analysis:

13 Copyright © 2004 SummarizationVisualization Large hr. of video Measure Normal Activity, Detect Whats Not Find pattern Few important events

14 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

15 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

16 Copyright © 2004

17 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

18 Copyright © 2004 Thank You Questions?

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