Outline <ul><li>Abstract </li></ul><ul><li>Introduction </li></ul><ul><li>In-Home Monitoring </li></ul><ul><li>Activity an...
Abstract <ul><li>Healthcare </li></ul><ul><ul><li>Aging in place </li></ul></ul><ul><ul><li>Automatic health monitoring </...
Introduction <ul><li>1.1  Overview </li></ul><ul><ul><li>1.1.1  The Activities of Daily Living Study </li></ul></ul><ul><u...
1.1.2  Simultaneous Tracking & Activity Recognition <ul><li>identifying people </li></ul><ul><li>tracking people as they m...
In-Home Monitoring : A Study of Case Managers
Activity and Location Inference
3.1 Overview of a typically instrumented room
3.2 A DBN describing room-level tracking and activity recognition
Figure 3.3: A DBN describing occupant state and data associations.
3.4 Accuracy vs. number of particles
Figure 3.5: Accuracy vs. number of occupants.
Figure 3.6: Accuracy vs. number of particles.
Figure 3.7: Tracking results for STAR experiment # 2.
Figure 3.8: Physical layout of the PlaceLab instrumented apartment.
Data Collection in the Home
4.1 Screenshot of CARS for experiment # 1
4.2 Symbols: (a) Refrigerator open, (b) water on, (c) cabinet closed
4.3 Pictures of (a) The iBracelet, a wearable RFID reader, (b) tagged objects
4.4 Screenshot of CARS for experiment # 2
4.5 Symbols from left to right: (a) Faucet, (b) bleach, (c) toothbrush
4.6 Relation between confidence and labeling accuracy
4.7 Model accuracy as number of trained episodes increases
Application to Activity Rating <ul><li>5.1 Introduction </li></ul><ul><li>5.2 Overview </li></ul><ul><li>5.3 Trace Repair ...
5.1 Trellis for k-Edits Viterbi on HMMs
5.2 Trellis for k-Edits Viterbi on HSMMs
5.3 HMMs vs. HSMMs (top) and HSMMs vs. TCHMMs (bottom)
 
 
5.4 The likelihood of KEDIT traces as k increases
Conclusion <ul><li>6.1 Summary </li></ul><ul><ul><li>6.1.1 The Activities of Daily Living Study </li></ul></ul><ul><ul><li...
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Assistive Intelligent Environments For Automatic Health Monitoring

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Assistive Intelligent Environments For Automatic Health Monitoring

  1. 2. Outline <ul><li>Abstract </li></ul><ul><li>Introduction </li></ul><ul><li>In-Home Monitoring </li></ul><ul><li>Activity and Location Inference </li></ul><ul><li>Data Collection in the Home </li></ul><ul><li>Application to Activity Rating </li></ul><ul><li>Conclusion </li></ul>
  2. 3. Abstract <ul><li>Healthcare </li></ul><ul><ul><li>Aging in place </li></ul></ul><ul><ul><li>Automatic health monitoring </li></ul></ul><ul><li>A particle filter : room-level tracking and activity recognition </li></ul><ul><li>“ context-aware recognition survey” : help users label anonymous episodes of activity for use as training examples in a supervised learner </li></ul><ul><li>The k-Edits Viterbi algorithm, which works within a Bayesian framework to automatically rate routine activities and detect irregular patterns of behavior </li></ul>
  3. 4. Introduction <ul><li>1.1 Overview </li></ul><ul><ul><li>1.1.1 The Activities of Daily Living Study </li></ul></ul><ul><ul><li>1.1.2 Simultaneous Tracking & Activity Recognition </li></ul></ul><ul><ul><li>1.1.3 The Context-Aware Recognition Survey </li></ul></ul><ul><ul><li>1.1.4 The k-Edits Viterbi Algorithm </li></ul></ul><ul><li>1.2 Thesis Contributions </li></ul><ul><li>1.3 Scenario </li></ul>
  4. 5. 1.1.2 Simultaneous Tracking & Activity Recognition <ul><li>identifying people </li></ul><ul><li>tracking people as they move </li></ul><ul><li>knowing what activities people are engaged in </li></ul><ul><li>recognizing when people deviate from regular patterns of behavior </li></ul><ul><li>providing advice on how activities could have been performed better </li></ul>
  5. 6. In-Home Monitoring : A Study of Case Managers
  6. 7. Activity and Location Inference
  7. 8. 3.1 Overview of a typically instrumented room
  8. 9. 3.2 A DBN describing room-level tracking and activity recognition
  9. 10. Figure 3.3: A DBN describing occupant state and data associations.
  10. 11. 3.4 Accuracy vs. number of particles
  11. 12. Figure 3.5: Accuracy vs. number of occupants.
  12. 13. Figure 3.6: Accuracy vs. number of particles.
  13. 14. Figure 3.7: Tracking results for STAR experiment # 2.
  14. 15. Figure 3.8: Physical layout of the PlaceLab instrumented apartment.
  15. 16. Data Collection in the Home
  16. 17. 4.1 Screenshot of CARS for experiment # 1
  17. 18. 4.2 Symbols: (a) Refrigerator open, (b) water on, (c) cabinet closed
  18. 19. 4.3 Pictures of (a) The iBracelet, a wearable RFID reader, (b) tagged objects
  19. 20. 4.4 Screenshot of CARS for experiment # 2
  20. 21. 4.5 Symbols from left to right: (a) Faucet, (b) bleach, (c) toothbrush
  21. 22. 4.6 Relation between confidence and labeling accuracy
  22. 23. 4.7 Model accuracy as number of trained episodes increases
  23. 24. Application to Activity Rating <ul><li>5.1 Introduction </li></ul><ul><li>5.2 Overview </li></ul><ul><li>5.3 Trace Repair for Hidden Markov Models </li></ul><ul><li>5.3.1 The Repaired MAP Path Estimation Problem </li></ul><ul><li>5.4 Trace Repair for Hidden Semi-Markov Models </li></ul><ul><li>5.5 Trace Repair for Constrained HMMs </li></ul><ul><li>5.6 Evaluation </li></ul><ul><li>5.7 Conclusions </li></ul>
  24. 25. 5.1 Trellis for k-Edits Viterbi on HMMs
  25. 26. 5.2 Trellis for k-Edits Viterbi on HSMMs
  26. 27. 5.3 HMMs vs. HSMMs (top) and HSMMs vs. TCHMMs (bottom)
  27. 30. 5.4 The likelihood of KEDIT traces as k increases
  28. 31. Conclusion <ul><li>6.1 Summary </li></ul><ul><ul><li>6.1.1 The Activities of Daily Living Study </li></ul></ul><ul><ul><li>6.1.2 Simultaneous Tracking & Activity Recognition </li></ul></ul><ul><ul><li>6.1.3 The Context-Aware Recognition Survey </li></ul></ul><ul><ul><li>6.1.4 The k-Edits Viterbi Algorithm </li></ul></ul>
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