Recognizing Human Activity from Sensor Data

1. Recognizing Human Activity from Sensor Data Henry Kautz University of WashingtonComputer Science & Engineering graduate students: Don Patterson, Lin Liao CSE faculty: Dieter Fox, Gaetano Borriello UW School of Medicine: Kurt Johnson Intel Research: Matthai Philipose, Tanzeem Choudhury

2. Converging Trends… • Pervasive sensing infrastructure • GPS enabled phones • RFID tags on all consumer products • Wireless motes • Breakthroughs in core artificial intelligence • After “AI boom” fizzled, basic science went on… • Advances in algorithms for probabilistic reasoning and machine learning • Bayesian networks • Stochastic sampling • Last decade: 10 variables  1,000,000 variables • Healthcare crisis • Epidemic of Alzheimer’s Disease • Deinstitutionalization of the cognitively disabled • Nationwide shortage of caretaking professionals

3. ...An Opportunity • Develop technology to • Support independent living by people with cognitive disabilities • At home • At work • Throughout the community • Improve health care • Long term monitoring of activities of daily living (ADL’s) • Intervention before a health crisis

4. The University of Washington Assisted Cognition Project • Synthesis of work in • Ubiquitous computing • Artificial intelligence • Human-computer interaction • ACCESS • Support use of public transit • CARE • ADL monitoring and assistance

5. This Talk • Building models of everyday plans and goals • From sensor data • By mining textual description • By engineering commonsense knowledge • Tracking and predicting a user’s behavior • Noisy and incomplete sensor data • Recognizing user errors • First steps toward proactive assistive technology

6. ACCESSAssisted Cognition in Community, Employment, & Support SettingsSupported by The National Institute on Disability & Rehabilitation Research (NIDDR)The National Science Foundation (NSF) Learning & Reasoning About Transportation Routines

7. Task • Given a data stream from a wearable GPS unit... • Infer the user’s location and mode of transportation (foot, car, bus, bike, ...) • Predict where user will go • Detect novel behavior • User errors? • Opportunities for learning?

8. Why Inference Is Not Trivial • People don’t have wheels • Systematic GPS error • We are not in the woods • Dead and semi-dead zones • Lots of multi-path propagation • Inside of vehicles • Inside of buildings • Not just location tracking • Mode, Prediction, Novelty

9. GPS Receivers We Used GeoStats wearable GPS logger Nokia 6600 Java Cell Phone with Bluetooth GPS unit

10. Geographic Information Systems Street map Data source: Census 2000 Tiger/line data Bus routes and bus stops Data source: Metro GIS

11. Architecture Learning Engine • Goals • Paths • Modes • Errors GIS Database Inference Engine

12. Probabilistic Reasoning • Graphical model: Dynamic Bayesian network • Inference engine: Rao-Blackwellised particle filters • Learning engine: Expectation-Maximization (EM) algorithm

13. Graphical Model (Version 1) • Transportation Mode • Velocity • Location • Block • Position along block • At bus stop, parking lot, ...? • GPS Offset Error • GPS signal

16. Rao-Blackwellised Particle Filtering • Inference: estimate current state distribution given all past readings • Particle filtering • Evolve approximation to state distribution using samples (particles) • Supports multi-modal distributions • Supports discrete variables (e.g.: mode) • Rao-Blackwellisation • Each particle includes a Kalman filter to represent distribution over positions • Improved accuracy with fewer particles

17. Tracking blue = foot green = bus red = car

18. Learning • User model = DBN parameters • Transitions between blocks • Transitions between modes • Learning: Monte-Carlo EM • Unlabeled data • 30 days of one user, logged at 2 second intervals (when outdoors) • 3-fold cross validation

19. Results

20. Prediction Accuracy How can we improve predictive power? Probability of correctly predicting the future City Blocks

21. Transportation Routines A B Work • Goals • work, home, friends, restaurant, doctor’s, ... • Trip segments • Home to Bus stop A on Foot • Bus stop A to Bus stop B on Bus • Bus stop B to workplace on Foot “Learning & Inferring Transportation Routines”, Lin Liao, Dieter Fox, & Henry Kautz, AAAI-2004 Best Paper Award

22. gk-1 gk tk-1 tk mk-1 mk xk-1 xk zk-1 zk Hierarchical Model Goal Trip segment Transportation mode x=<Location, Velocity> GPS reading

23. Hierarchical Learning • Learn flat model • Infer goals • Locations where user is often motionless • Infer trip segment begin / end points • Locations with high mode transition probability • Infer trips segments • High-probability single-mode block transition sequences between segment begin / end points • Perform hierarchical EM learning

24. Inferring Goals

25. Inferring Trip Segments Going to work Going home

26. Correct goal and route predicted 100 blocks away

27. Novelty & Error Detection • Approach: model-selection • Run several trackers in parallel • Tracker 1: learned hierarchical model • Tracker 2: untrained flat model • Tracker 3: learned model with clamped final goal • Estimate the likelihood of each tracker given the observations

28. Detect User Errors Untrained Trained Instantiated

29. Application:Opportunity Knocks Demonstration (by Don Patterson) at AAHA Future of Aging Services, Washington, DC, March, 2004

30. CARECognitive Assistance in Real-world Environmentssupported by the Intel Research Council Learning & Inferring Activities of Daily Living

31. Research Hypothesis • Observation: activities of daily living involve the manipulation of many physical objects • Cooking, cleaning, eating, personal hygiene, exercise, hobbies, ... • Hypothesis: can recognize activities from a time-sequence of object “touches” • Such models are robust and easily learned or engineered

32. Sensing Object Manipulation • RFID: Radio-frequency ID tags • Small • Semi-passive • Durable • Cheap

33. Where Can We Put Tags?

34. How Can We Sense Them? coming... wall-mounted “sparkle reader”

35. Example Data Stream

36. Making Tea

37. Building Models • Core ADL’s amenable to classic knowledge engineering • Open-ended, fine-grained models: infer from natural language texts? • Perkowitz et al., “Mining Models of Human Activities from the Web”, WWW-2004

38. Experimental Setup • Hand-built library of 14 ADL’s • 17 test subjects • Each asked to perform 12 of the ADL’s • Data not segmented • No training on individual test subjects

39. General Solution Quantitative Results 95/84 Point Solution Quantitative Results General Solution Anecdotal Results Point Solution Anecdotal Results Pervasive Computing, Oct-Dec 2004

40. Current Directions • Affective & physiological state • agitated, calm, attentive, ... • hungry, tired, dizzy, ... • Interactions between people • Human Social Dynamics • Principled human-computer interaction • Decision-theoretic control of interventions

41. Why Now? • A goal of much work of AI in the 1970’s was to create programs that could understand the narrative of ordinary human experience • This area pretty much disappeared • Missing probabilistic tools • Systems not able to experience world • Lacked focus – “understand” to what end? • Today: tools, grounding, motivation

42. Challenge to Nanotechnology Community • Current sensors detect physical or physiological state: user mental state must be indirectly inferred • To what can extend can nanotechnology afford direct access to a person’s emotions and intentions?