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Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

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Author(s)
Argent, Rob 
Drummond, Sean 
Remus, Alex 
O'Reilly, Martin 
Caulfield, Brian 
Uri
http://hdl.handle.net/10197/11040
Date Issued
19 August 2019
Date Available
26T14:12:36Z August 2019
Abstract
Introduction: Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods: Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results: Average root-mean-squared error for the best performing algorithms across all exercises was 4.81 (SD ¼ 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99 (SD ¼ 1.83). Conclusions: Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.
Sponsorship
European Commission Horizon 2020
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Journal Article
Publisher
Sage
Journal
Journal of Rehabilitation and Assistive Technologies Engineering
Volume
6
Start Page
1
End Page
10
Copyright (Published Version)
2019 the Authors
Keywords
  • Joint angle

  • Wearable sensor

  • Range of motion

  • Inertial measurement ...

  • Biomechanics

  • Machine learning

  • Neural networks

DOI
10.1177/2055668319868544
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
Views
744
Acquisition Date
Mar 30, 2023
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Downloads
249
Last Month
11
Acquisition Date
Mar 30, 2023
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