Motion feature simulation based on wearable sensors and global search algorithms

Abstract

With the development of wearable technology, it is often difficult to accurately describe motion features with sensor data alone. To solve these problems, a simulation method based on global search algorithm is proposed, which combines sensor data and global search algorithm to capture motion features better. On the basis of analyzing the advantages and disadvantages of traditional motion feature extraction methods, this paper uses a global search algorithm to optimize sensor data to eliminate noise and fill data gaps, and get more accurate motion features. Through in-depth study of machine learning related theories, a model is established and its characteristics are deeply explored. The experimental results show that the simulation method based on wearable sensor and global search algorithm can effectively simulate motion features. Compared with the traditional analysis method based on sensor raw data, the proposed method can provide more accurate and comprehensive motion feature analysis results, reduce workload and time cost, and improve motion efficiency.