Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm

Human behavior pattern recognition (BPR) from accelerometer signals is a challenging problem due to variations in signal durations of different behaviors. Analysis of human behaviors provides in depth observations of subject's routines, energy consumption and muscular stress. Such observations hold key importance for the athletes and physically ailing humans, who are highly sensitive to even minor injuries. A novel idea having variant of genetic algorithm is proposed in this paper to solve complex feature selection and classification problems using sensor data. The proposed BPR system, based on statistical dependencies between behaviors and respective signal data, has been used to extract statistical features along with acoustic signal features like zero crossing rate to maximize the possibility of getting optimal feature values. Then, reweighting of features is introduced in a feature selection phase to facilitate the segregation of behaviors. These reweighted features are further processed by biological operations of crossover and mutation to adapt varying signal patterns for significant accuracy results. Experiments on wearable sensors benchmark datasets HMP, WISDM and self-annotated IMSB datasets have been demonstrated to testify the efficacy of the proposed work over state-of-the-art methods.