Recognition of different daily living activities using hidden Markov model regression

The human activity recognition is widely used for human behavior prediction especially for dependent people. This is achieved to provide safety, health monitoring, and well being of this population at home. In this paper, the problem of human activity recognition is reformulated as joint segmentation of multidimensional time series. The hidden Markov model regression (HMMR) is used to perform unsupervised segmentation strategy between activities using the expectation-maximization algorithm. This is accomplished over six logical scenarios of twelve daily activities such as stair descent, standing, sitting down, sitting, From sitting to sitting on the ground and sitting on the ground. To evaluate the performance of HMMR model, other unsupervised methods are used including K-means, Gaussian mixtures model and the hidden Markov model. The results show that the HMMR model provides the best results for the different scenarios with up to 97% in terms of correct classification rate.