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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