Wearable Device-Based Data Collection and Feature Analysis Method for Outdoor Sports

Abstract

In recent years, with the rapid popularization of smart phones and wearable smart devices, it is no longer difficult to obtain a large number of human motion data related to people's heart rate and geographical location, which has spawned a series of running fitness applications, leading to the national running wave and promoting the rapid development of the sports industry. Based on the long short-term memory cyclic neural network, this paper processes, identifies, and analyzes the motion data collected by wearable devices. Through massive data training, a set of accurate auxiliary models of outdoor sports is obtained to help optimize and improve the effect of outdoor sports. The results show that the method proposed in this paper has a higher degree of sports action and feature recognition and can better assist in the completion of outdoor sports.