Assessing movement quality on straight leg raise using neural networks and data science

D, Dopazo, Button, Kate ORCID: https://orcid.org/0000-0003-1073-9901 and Al-Amri, Mohammad ORCID: https://orcid.org/0000-0003-2806-0462 2022. Assessing movement quality on straight leg raise using neural networks and data science. Presented at: 2022 OARSI World Conference for Osteoarthritis, Berlin, 7-10 April, 2022. Osteoarthritis and Cartilage. , vol.30 10.1016/j.joca.2022.02.116

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Abstract

Wearable sensors used to measure position/orientation and acceleration during exercise for knee and hip osteoarthritis have the potential to enhance physiotherapy rehabilitation through the personalisation of exercise. This data can be used to monitor exercise performance from the home and provide personalised feedback based on the quality of movement during the exercises outside of the clinical setting. Data science can be implemented to objectively characterise the quality of the movement patterns. This is achieved with intelligent algorithms that identify the movement quality based on orientations and acceleration data, which represent common feedback given by physiotherapists. These algorithms aim to recognize the underlying relationships inside the data, emulating the way the human brain functions. This results in a classification system that can distinguish between a good and a difficult movement.This study aimed to develop a neural network to assess the quality of movement during one rehabilitation exercise, the straight leg raise. This exercise was selected because it is a commonly prescribed non-weight bearing exercise used early in a rehabilitation programme.

Item Type:	Conference or Workshop Item (Speech)
Status:	Published
Schools:	Healthcare Sciences
ISSN:	1063-4584
Funders:	Accelerate
Last Modified:	30 Jan 2024 11:14
URI:	https://orca.cardiff.ac.uk/id/eprint/155003

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