Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors

TLDR

Motion‑analysis technologies are widely used to monitor injury risk and improve performance, yet they are costly, confined to laboratory settings, and typically evaluate only a few trials of each movement. This study introduces an ambulatory motion‑analysis framework that employs wearable inertial sensors to automatically assess all athlete activities in outdoor training environments and to evaluate movement technique. The framework first classifies a broad range of training activities using a Discrete Wavelet Transform feature extractor combined with a Random Forest classifier, then estimates sensor orientations via a gradient‑descent algorithm to compute knee flexion‑extension angles, and finally applies a curve‑shift registration technique to generate normative data and detect deviations indicative of injury risk. The activity classifier achieved up to 98 % accuracy in distinguishing various sports movements.

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 an outdoor training environment. We firstly 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. Secondly, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the thigh and shank of a subject, from which the flexion-extension knee angle is calculated. Finally, a curve shift registration technique is applied to both generate normative data and determine if a subject's movement technique differed to the normative data in order to identify potential injury related factors. 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.