A machine learning classification model for monitoring the daily physical behaviour of lower-limb amputees

Griffiths, BN; Diment, L; Granat, MH

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.

Citation

Griffiths, B., Diment, L., & Granat, M. (2021). A machine learning classification model for monitoring the daily physical behaviour of lower-limb amputees. Sensors, 21(22), e7458. https://doi.org/10.3390/s21227458

• Journal Article Type: Article
• Acceptance Date: Nov 5, 2021
• Publication Date: Nov 10, 2021
• Deposit Date: Nov 12, 2021
• Publicly Available Date: Nov 12, 2021
• Journal: Sensors
• Publisher: MDPI
• Volume: 21
• Issue: 22
• Pages: e7458
• DOI: https://doi.org/10.3390/s21227458
• Publisher URL: https://doi.org/10.3390/s21227458
• Related Public URLs: http://www.mdpi.com/journal/sensors
• Additional Information: 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; Funders : Engineering and Physical Sciences Research Council (EPSRC);National Institute for Health Research (NIHR);Engineering and Physical Sciences Research Council;National Institute for Health Research; Projects : A Step Change in LMIC Prosthetics Provision through Computer Aided Design, Actimetry and Database Technologies;EP/R014213/1;Global Challenges Research Fund; Grant Number: EP/R014213/1