Human Activity Recognition Using Grammar-based Genetic Programming

Smart devices provide a way of acquiring useful data for human activity recognition (HAR). The identification of activities is a task applicable to a wide range of situations, such as automatically providing aid to someone in need. Machine learning techniques can solve this problem, but their capacity in providing understanding regarding the classification is usually limited. Here, we propose a Grammar-based Genetic Programming (GGP) to generate interpretable models for HAR. A Context-free Grammar defines a language that the models belong to, providing a way to read and extract knowledge. The results show that the proposed GGP generates results better than another Genetic Programming method and machine learning approaches. Also, the models created provided an understanding of the features associated with the activities.