A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare

Recently, human healthcare from body sensor data has been getting remarkable research attentions by a huge range of human-computer interaction and pattern analysis researchers due to its practical applications such as smart health care systems. For example, smart wearable-based behavior recognition system can be used to assist the rehabilitation of patients in a smart clinic to improve the rehabilitation process and to prolong their independent life. Although there are many ways of using distributed sensors to monitor vital signs and behavior of people, physical human action recognition via body sensors provides valuable data regarding an individual's functionality and lifestyle. In this work, we propose a body sensor-based system for behavior recognition using deep Recurrent Neural Network (RNN), a promising deep learning algorithm based on sequential information. We perform data fusion from multiple body sensors such as electrocardiography (ECG), accelerometer, magnetometer, etc. The extracted features are further enhanced via kernel principal component analysis (KPCA). The robust features are then used to train an activity RNN, which is later used for behavior recognition. The system has been compared against the conventional approaches on three publicly available standard datasets. The experimental results show that the proposed approach outperforms the available state-of-the-art methods.