CHAR-HMM: An Improved Continuous Human Activity Recognition Algorithm Based on Hidden Markov Model.

With the rapid development of wearable sensor technology, Human Activity Recognition (HAR) based on sensor data has attracted more and more attentions. The Hidden Markov Model (HMM) with perfect performance in speech recognition has a good effect on HAR. However, almost all these techniques train multiple Hidden Markov Models for different classes of activity. For a given activity sequence with multiple activities, the activity corresponding to the HMM with the maximum generating probability is selected as the recognition result, which is not suitable for continuous HAR with multiple activities. For this problem, we propose an improved Hidden Markov activity recognition algorithm where discriminative model and generative model are utilized. The discriminative model SVM is used to produce the observation sequence of HMM, and the generative model HMM is used to generate the final result. Compared with the traditional Hidden Markov HAR model, our proposal has good performance in terms of precision, recall and F1 score.