Title:
activity-recognition--based-on-hmm
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Description: The HMM is a statistical approach in which the underlying model is a stochastic Markovian
process that is not observable (i.e., hidden) whic h can be observed through other processes that
produce the sequence of observed (emitted) features. In our HMM we let the hidden nodes represent
activities. The observable nodes re present combinations of the features described earlier. The
probabilistic relationships between hidden nodes and observable nodes and the probabilistic transition
between hidden nodes are estimated by the relative fr equency with which these relationships occur in
the sample data. An example HMM for three of the activities is shown in Figure 3. Given an input
sequence of sensor events, our algorithm finds the mo st likely sequence of hidden states, or activities,
which could have generated the observed event sequence. We use the Viterbi algorithm to
identify this sequence of hidden states.
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File list (Check if you may need any files):
src
...\Makefile
...\ar.c
...\ar.h
...\crf.c
...\crf.h
...\hmm.c
...\hmm.h
...\lbfgs.c
...\lbfgs.h
...\nb.c
...\nb.h
...\nb.o
