Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session

TLDR

Motion analysis technologies are widely used to monitor injury risk and improve performance, yet most are costly, lab‑bound, and limited to a few trials. This study introduces an ambulatory motion‑analysis framework that uses wearable inertial sensors to assess all of an athlete’s activities in a real training environment. The framework automatically classifies activities with a discrete wavelet transform and random‑forest classifier, estimates sensor orientations via gradient‑descent to compute joint angles and sacrum accelerations, and compares these metrics to normative data using curve‑shift registration. The classifier achieved up to 98 % accuracy, demonstrating that the framework can reliably classify sports activities and evaluate movement technique in unconstrained settings for injury management and performance enhancement.

Abstract

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion–extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.