string similarity techniques to detect activity daily life in smart homes equipped with a binary sensor network

Human activity recognition can be employed in systems that provide support to people who need special care in their daily activities. Various machine learning methods, including Hidden Markov Models and their extensions, are widely used to deal with this problem. In the previous works, HMM-based methods use the conditional independent assumption to compute the probability of a segment of observations. In this research, instead of conditional independent assumption, a new probabilistic model for string space, based on Dynamic Time Warping and Weighted Levenstein Distance is proposed. The model, combined with the Hidden semi Markov Model, is applied to a publicly available dataset. The results show considerable improvements in comparison with using the Hidden semi Markov Model independently. The proposed models are flexible and can work together with other probabilistic models.