Imperial College London
Alternate

ProfessorFernandoBello

Faculty of Medicine, Department of Surgery & Cancer

Professor of Surgical Computing and Simulation Science
 
 
 
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Contact

 

+44 (0)20 3315 8231f.bello Website

 
 
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Location

 

G3.50Chelsea and Westminster HospitalChelsea and Westminster Campus

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Summary

 

Publications

Citation

BibTex format

@article{Haghighi:2020:10.2196/17289,
author = {Haghighi, Osgouei R and Soulsby, D and Bello, F},
doi = {10.2196/17289},
journal = {JMIR Rehabilitation and Assistive Technologies},
title = {Rehabilitation Exergames: use of motion sensing and machine learning to quantify exercise performance in healthy volunteers},
url = {http://dx.doi.org/10.2196/17289},
volume = {7},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background:Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well or not patients are performing the prescribed tasks. The absence of proper feedback might result in patients doing the exercises incorrectly, which could worsen their condition.Objective:We propose the use of two machine learning algorithms, namely Dynamic Time Warping (DTW) and Hidden Markov Model (HMM), to quantitively assess the patient’s performance with respects to a reference.Methods:Movement data were recorded using a Kinect depth sensor, capable of detecting 25 joints in the human skeleton model, and were compared to those of a reference. 16 participants were recruited to perform four different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand. Their performance was compared to that of a physiotherapist as a reference.Results:Both algorithms show a similar trend in assessing participants' performance. However, their sensitivity level was different. While DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details.Conclusions:The chosen algorithms demonstrated their capacity to objectively assess physical therapy performances. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whilst DTW could be used later on to focus on the detail.
AU  - Haghighi,Osgouei R
AU  - Soulsby,D
AU  - Bello,F
DO  - 10.2196/17289
PY  - 2020///
SN  - 2369-2529
TI  - Rehabilitation Exergames: use of motion sensing and machine learning to quantify exercise performance in healthy volunteers
T2  - JMIR Rehabilitation and Assistive Technologies
UR  - http://dx.doi.org/10.2196/17289
UR  - https://preprints.jmir.org/preprint/17289/accepted
UR  - http://hdl.handle.net/10044/1/79996
VL  - 7
ER  -
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