Please use this identifier to cite or link to this item: https://doi.org/10.1177/2055668319868544
Title: Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor
Authors: Argent, Rob
Drummond, Sean
Remus, Alexandria 
O'Reilly, Martin
Caulfield, Brian
Keywords: Science & Technology
Technology
Engineering, Biomedical
Engineering
Joint angle
wearable sensor
range of motion
inertial measurement unit
biomechanics
machine learning
neural networks
ABSOLUTE ERROR MAE
UNIVERSAL GONIOMETER
KNEE RANGE
RELIABILITY
MOTION
VALIDITY
RMSE
Issue Date: Aug-2019
Publisher: SAGE PUBLICATIONS INC
Citation: Argent, Rob, Drummond, Sean, Remus, Alexandria, O'Reilly, Martin, Caulfield, Brian (2019-08). Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor. JOURNAL OF REHABILITATION AND ASSISTIVE TECHNOLOGIES ENGINEERING 6. ScholarBank@NUS Repository. https://doi.org/10.1177/2055668319868544
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 = 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 = 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.
Source Title: JOURNAL OF REHABILITATION AND ASSISTIVE TECHNOLOGIES ENGINEERING
URI: https://scholarbank.nus.edu.sg/handle/10635/239360
ISSN: 2055-6683
DOI: 10.1177/2055668319868544
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