assistive intelligent environments for automatic health monitoring

Outline

Abstract Introduction In-Home Monitoring Activity and Location Inference Data Collection in the Home Application to Activity Rating Conclusion

Abstract Healthcare

Aging in placeAutomatic health monitoring

A particle filter : room-level tracking and activity recognition

“context-aware recognition survey” : help users label anonymous episodes of activity for use as training examples in a supervised learner

The k-Edits Viterbi algorithm, which works within a Bayesian framework to automatically rate routine activities and detect irregular patterns of behavior

Introduction

1.1 Overview1.1.1 The Activities of Daily Living

Study1.1.2 Simultaneous Tracking &

Activity Recognition1.1.3 The Context-Aware Recognition

Survey1.1.4 The k-Edits Viterbi Algorithm

1.2 Thesis Contributions 1.3 Scenario

1.1.2 Simultaneous Tracking & Activity Recognition identifying people tracking people as they move knowing what activities people are

engaged in recognizing when people deviate

from regular patterns of behavior providing advice on how activities

could have been performed better

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 5.1 Introduction 5.2 Overview 5.3 Trace Repair for Hidden Markov

Models 5.3.1 The Repaired MAP Path

Estimation Problem 5.4 Trace Repair for Hidden Semi-

Markov Models 5.5 Trace Repair for Constrained HMMs 5.6 Evaluation 5.7 Conclusions

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

6.1 Summary6.1.1 The Activities of Daily Living

Study6.1.2 Simultaneous Tracking & Activity

Recognition6.1.3 The Context-Aware Recognition

Survey6.1.4 The k-Edits Viterbi Algorithm