Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Towards automatic activity classification and movement assessment during a sports training session

Ahmadi, Amin, Mitchell, Edmond, Richter, Chris, Destelle, Francois, Gowing, Marc, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 (2014) Towards automatic activity classification and movement assessment during a sports training session. IEEE Internet of Things, 2 (1). pp. 23-32. ISSN 2327-466

Abstract
Abstract—Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete perfor- mance. 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 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 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 is 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.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Biomechanics
Subjects:Engineering > Electronic engineering
Medical Sciences > Physiology
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/JIOT.2014.2377238
Copyright Information:© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI Grant Number 12/RC/2289
ID Code:20593
Deposited On:27 May 2015 09:48 by Kevin Fraser . Last Modified 19 Oct 2018 12:32
Metrics

Altmetric Badge

Dimensions Badge

Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record