Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation
BackgroundPhysiotherapy is a critical element in the successful conservative management of low back pain (LBP). A gold standard for quantitatively measuring physiotherapy participation is crucial to understanding physiotherapy adherence in managing recovery from LBP....
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
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JMIR Publications
2022-08-01
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Series: | JMIR Rehabilitation and Assistive Technologies |
Online Access: | https://rehab.jmir.org/2022/3/e38689 |
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author | Abdalrahman Alfakir Colin Arrowsmith David Burns Helen Razmjou Michael Hardisty Cari Whyne |
author_facet | Abdalrahman Alfakir Colin Arrowsmith David Burns Helen Razmjou Michael Hardisty Cari Whyne |
author_sort | Abdalrahman Alfakir |
collection | DOAJ |
description |
BackgroundPhysiotherapy is a critical element in the successful conservative management of low back pain (LBP). A gold standard for quantitatively measuring physiotherapy participation is crucial to understanding physiotherapy adherence in managing recovery from LBP.
ObjectiveThis study aimed to develop and evaluate a system with wearable inertial sensors to objectively detect the performance of unsupervised exercises for LBP comprising movement in multiple planes and sitting postures.
MethodsA quantitative classification design was used within a machine learning framework to detect exercise performance and posture in a cohort of healthy participants. A set of 8 inertial sensors were placed on the participants, and data were acquired as they performed 7 McKenzie low back exercises and 3 sitting posture positions. Engineered time series features were extracted from the data and used to train 9 models by using a 6-fold cross-validation approach, from which the best 2 models were selected for further study. In addition, a convolutional neural network was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed the most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and best performing algorithms for exercise and posture classification. The final models were evaluated using the F1 score in a 10-fold cross-validation approach.
ResultsIn total, 19 healthy adults with no history of LBP each completed at least one full session of exercises and postures. Random forest and XGBoost (extreme gradient boosting) models performed the best out of the initial set of 9 engineered feature models. The optimal hardware configuration was identified as a 3-sensor setup—lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XGBoost model achieved the highest exercise (F1 score: mean 0.94, SD 0.03) and posture (F1 score: mean 0.90, SD 0.11) classification scores. The convolutional neural network achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1 score: mean 0.94, SD 0.02) and the accelerometer channel alone for posture classification (F1 score: mean 0.88, SD 0.07).
ConclusionsThis study demonstrates the potential of a 3-sensor lower body wearable solution (eg, smart pants) that can identify exercises in multiple planes and proper sitting postures, which is suitable for the treatment of LBP. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring. |
first_indexed | 2024-03-12T12:49:19Z |
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id | doaj.art-2a1031f41525430f969fed04647b22ab |
institution | Directory Open Access Journal |
issn | 2369-2529 |
language | English |
last_indexed | 2024-03-12T12:49:19Z |
publishDate | 2022-08-01 |
publisher | JMIR Publications |
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series | JMIR Rehabilitation and Assistive Technologies |
spelling | doaj.art-2a1031f41525430f969fed04647b22ab2023-08-28T22:56:06ZengJMIR PublicationsJMIR Rehabilitation and Assistive Technologies2369-25292022-08-0193e3868910.2196/38689Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and ValidationAbdalrahman Alfakirhttps://orcid.org/0000-0003-0293-3739Colin Arrowsmithhttps://orcid.org/0000-0003-1938-0210David Burnshttps://orcid.org/0000-0002-1617-596XHelen Razmjouhttps://orcid.org/0000-0002-3162-4241Michael Hardistyhttps://orcid.org/0000-0002-8941-3543Cari Whynehttps://orcid.org/0000-0002-6822-8314 BackgroundPhysiotherapy is a critical element in the successful conservative management of low back pain (LBP). A gold standard for quantitatively measuring physiotherapy participation is crucial to understanding physiotherapy adherence in managing recovery from LBP. ObjectiveThis study aimed to develop and evaluate a system with wearable inertial sensors to objectively detect the performance of unsupervised exercises for LBP comprising movement in multiple planes and sitting postures. MethodsA quantitative classification design was used within a machine learning framework to detect exercise performance and posture in a cohort of healthy participants. A set of 8 inertial sensors were placed on the participants, and data were acquired as they performed 7 McKenzie low back exercises and 3 sitting posture positions. Engineered time series features were extracted from the data and used to train 9 models by using a 6-fold cross-validation approach, from which the best 2 models were selected for further study. In addition, a convolutional neural network was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed the most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and best performing algorithms for exercise and posture classification. The final models were evaluated using the F1 score in a 10-fold cross-validation approach. ResultsIn total, 19 healthy adults with no history of LBP each completed at least one full session of exercises and postures. Random forest and XGBoost (extreme gradient boosting) models performed the best out of the initial set of 9 engineered feature models. The optimal hardware configuration was identified as a 3-sensor setup—lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XGBoost model achieved the highest exercise (F1 score: mean 0.94, SD 0.03) and posture (F1 score: mean 0.90, SD 0.11) classification scores. The convolutional neural network achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1 score: mean 0.94, SD 0.02) and the accelerometer channel alone for posture classification (F1 score: mean 0.88, SD 0.07). ConclusionsThis study demonstrates the potential of a 3-sensor lower body wearable solution (eg, smart pants) that can identify exercises in multiple planes and proper sitting postures, which is suitable for the treatment of LBP. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring.https://rehab.jmir.org/2022/3/e38689 |
spellingShingle | Abdalrahman Alfakir Colin Arrowsmith David Burns Helen Razmjou Michael Hardisty Cari Whyne Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation JMIR Rehabilitation and Assistive Technologies |
title | Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation |
title_full | Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation |
title_fullStr | Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation |
title_full_unstemmed | Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation |
title_short | Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation |
title_sort | detection of low back physiotherapy exercises with inertial sensors and machine learning algorithm development and validation |
url | https://rehab.jmir.org/2022/3/e38689 |
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