Griffiths, BN, Diment, L and Granat, MH 
ORCID: https://orcid.org/0000-0002-0722-2760
2021,
'A machine learning classification model for monitoring the daily physical behaviour of lower-limb amputees'
, Sensors, 21 (22)
, e7458.
|
PDF
- Published Version
Available under License Creative Commons Attribution 4.0. Download (3MB) | Preview |
Abstract
There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users.
| Item Type: | Article |
|---|---|
| Contributors: | Sacchetti, M (Editor) |
| Additional Information: | ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1424-8220 **History: published 10-11-2021; accepted 05-11-2021 |
| Schools: | Schools > School of Health and Society |
| Journal or Publication Title: | Sensors |
| Publisher: | MDPI |
| ISSN: | 1424-8220 |
| Related URLs: | |
| Funders: | Engineering and Physical Sciences Research Council (EPSRC), National Institute for Health Research (NIHR) |
| SWORD Depositor: | Publications Router |
| Depositing User: | Publications Router |
| Date Deposited: | 12 Nov 2021 09:30 |
| Last Modified: | 15 Feb 2022 17:05 |
| URI: | http://usir.salford.ac.uk/id/eprint/62346 |
Actions (login required)
 |
Edit record (repository staff only) |
Tools
Tools