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Most existing human activity classification systems require a large training dataset to construct statistical models for each activity of interest. This may be impractical in many cases. In this paper, we proposed a semi-supervised HMM based activity monitoring system, that adapts the HMM for a specific subject from a general model in order to alleviate the requirement of a large training data set...
In this paper, we describe an application of hidden Markov models (HMMs) to the problem of time-locating specific events in normal gait movement patterns. The use of HMMs in this paper is mainly related to the opportunity they offer to segment gait data collected at different walking speeds and inclinations of the walking surface. A simple four-state left-right HMM is trained on a dataset of signals...
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