Accelerometer-based algorithm for the segmentation and classification of repetitive human movements during workouts

Abstract

Monitoring a person’s physical activity has a wide range of applications in both sports and medicine. With the advancement of technology for measuring human movement, it is possible to monitor the performed activity without a need for an expert to directly overlook the trainee. While the initial interest focused mainly on aerobic exercises, research has recently begun to focus on strength exercises. The goal is to achieve the highest possible accuracy in tracking movement while maintaining the low cost and energy autonomy of the monitoring device. In this paper, an algorithm for the segmentation and classification of repetitive movements during workouts based on 3-axis accelerometer data from a wearable device is presented. The accelerometer signals were recorded continuously during the workout session which consisted typically of 9 strength exercises, where 8 default movements were repeated in three sets. Segmentation of the acceleration signals recorded during the workout was done using the frequency spectrum of the acceleration magnitude with an accuracy of 99.4%, while the classification of the segmented movements was done using the Dynamic Time Warping (DTW) algorithm with an accuracy of 85.7%.