A Novel Sparse Representation and Softmax Method for Human Activity Identification in Healthcare Systems

Human activity identification has been attracting extensive research attention due to its prominent applications in healthcare systems such as healthcare monitoring and rehabilitation process. Traditional methods are greatly dependent on hand-crafted feature extraction, hampering their generalization performance. In this research, a novel sparse representation and softmax (SRS) method is presented for human activity identification to reduce the computation complexity of the task and improve the accuracy of classification. The multi-class classifier based on the softmax function is firstly introduced to improve sensor data classification performance. Sparse representation technology is then applied in our work to extract human activity features from sensor data. The output of the classifier model, taking raw sensor data after transforming into a high-dimensional feature space as input, provides a normalization of the probability distribution of activity categories, thereby ensuring accuracy and efficiency under diverse human activities. Experiments on a collection of raw sensor data from wireless sensor networks demonstrate the identification accuracy of our approach compared with nearest neighbor, naive Bayesian classifier, and support vector machine methods. The F1-score of the proposed method is respectively 14.1%, 19.6%, and 6.8% higher than the approaches mentioned above, indicating the effectiveness of SRS.