Methodology for Human Activity Recognition Based on Wearable Sensor Networks

This chapter explores the application of wearable sensor networks in capturing human movement and recognizing human activities, which are crucial components in advancing intelligent healthcare and sports analytics. The text presents an overview of the requisite sensor technology, materials employed, and methods for thorough data processing to precisely capture and interpret human motion. For motion capture, the chapter introduces data fusion techniques, initial pose adjustment strategies, and refined motion capture algorithms, all of which have been put to the test and validated. In the arena of human activity recognition, the text proposes a K-SVD-based method that builds on a framework encompassing data preprocessing, feature extraction, feature selection, and the application of recognition algorithms. Moreover, the chapter revisits the practical benefits that human motion capture technology and human activity recognition bring to the table. In healthcare, these technologies foster the possibility of real-time monitoring and proactive care for patients. In the realm of sports, they provide comprehensive performance analytics and facilitate tailored training regimens.