Automatic low back pain classification using inertial measurement unit sensors: a preliminary analysis

Bacon, Zoe, Hicks, Yulia ORCID: https://orcid.org/0000-0002-7179-4587, Al-Amri, Mohammad ORCID: https://orcid.org/0000-0003-2806-0462 and Sheeran, Liba ORCID: https://orcid.org/0000-0002-1502-764X 2020. Automatic low back pain classification using inertial measurement unit sensors: a preliminary analysis. Procedia Computer Science 176 , pp. 2822-2831. 10.1016/j.procs.2020.09.272

Preview

PDF - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

Abstract

Low back pain (LBP) is a major health problem that has now become leading cause of disability worldwide. The majority of LBP has no specific pathological cause. Classification of non-specific LBP (NSLBP) into subgroups corresponding to the reported symptoms has been identified as an essential step towards the provision of personalised management and rehabilitation plans. Currently, clinicians classify low back pain patients into clinical subgroups based on clinical judgement and expertise, which is a time-consuming process open to human error. This paper introduces a novel approach for automatic classification of NSLBP patients into clinical subgroups on the basis of the MTw2 inertial measurement unit (MTw2 IMU tracker) motion data, which are portable units and thus desirable for clinical use. Four MTw2 IMU trackers tracking movement during a number of physical assessment tests were investigated in their ability to distinguish between clinically recognized NSLBP subgroups. Simple motion features such as the angular range of displacement were used in classification experiments to reflect how clinicians make decisions when classifying NSLBP. The achieved results were comparable to the state of art results in automatic NSLBP classification using optical motion capture data and demonstrated the feasibility of developing an automatic classification system on the basis of the MTw2 IMU tracker motion data obtained with an individual performing a battery of standard physical assessment tests. Further developments could address gaps in current medical and engineering literature and improve clinical outcomes.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Healthcare Sciences Engineering Arthritis Biomechanics Bioengineering Centre (ARUKBBC)
Additional Information:	This is an open access article under the CC-BY-NC-ND 4.0 International license.
Publisher:	Elsevier
ISSN:	1877-0509
Funders:	Health and Care Research Wales, RCBC Wales
Date of First Compliant Deposit:	4 February 2021
Date of Acceptance:	3 September 2020
Last Modified:	05 May 2023 08:47
URI:	https://orca.cardiff.ac.uk/id/eprint/131305

Actions (repository staff only)

Edit Item