Continuous Recognition of Daily Activities from Multiple Heterogeneous Sensors
Date Issued
2008
Date
2008
Author(s)
Wu, Tsu-Yu
Abstract
Recognition of daily activities is an enabling technology for active service providing and automatic in-home monitoring. In this thesis, we aim to recognize activities in a long sensor stream without knowing the boundary of activities. We formulate this continuous recognition problem as a sequence labeling problem. The activity is labeled every a fixed interval given the sensor readings.using multiple heterogeneous sensors helps disambiguate different activities. However, these sensors are very diverse in readings. To evaluate the capability of models in dealing with such diverse sensors, we compare several state-of-the-art sequence labeling algorithms including hidden Markov model (HMM), linear-chain conditional random field (LCRF) and SVMhmm. The results show that the two discriminative models, LCRF and SVMhmm, significantly outperform HMM. SVM$^{hmm}$ show robustness in dealing with all sensors we used. By incorporating proper overlapping features, the accuracy can be further improved. In additions, CRF and SVMhmm perform comparably with these overlapping features.or active service providing, we evaluate various inference strategies for the on-line recognition problem. On-line Viterbi algorithm achieves highest frame accuracy but suffers from high insertion errors that may cause unexpected services. We propose smooth on-line Viterbi algorithm to solve this problem.
Subjects
Activity Recognition
Heterogeneous Sensors
Continuous Recognition
Hidden Markov Model
Conditional Random Field
Structural Support Vector Machine
Type
thesis
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