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Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Abstract ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1.1.2  Simultaneous Tracking & Activity Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object]

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