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

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Title: Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor
Authors: Argent, RobDrummond, SeanRemus, AlexO'Reilly, MartinCaulfield, Brian
Permanent link: http://hdl.handle.net/10197/11040
Date: 19-Aug-2019
Online since: 2019-08-26T14:12:36Z
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.
Funding Details: European Commission Horizon 2020
Science Foundation Ireland
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 angleWearable sensorRange of motionInertial measurement unitBiomechanicsMachine learningNeural networks
DOI: 10.1177/2055668319868544
Language: en
Status of Item: Peer reviewed
Appears in Collections:Public Health, Physiotherapy and Sports Science Research Collection
Insight Research Collection

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