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• W4225137067 abstract "<sec> <title>BACKGROUND</title> Physiotherapy is a critical element in successful conservative management of low back pain (LBP). </sec> <sec> <title>OBJECTIVE</title> The aim of this study was to develop and evaluate a system with wearable inertial sensors to objectively detect sitting postures and performance of unsupervised exercises for LBP containing movement in multiple planes. </sec> <sec> <title>METHODS</title> A set of 8 inertial sensors were placed on 19 healthy adult subjects. Data was acquired as they performed 7 McKenzie low-back exercises and 3 sitting posture positions. This data was used to train two models (Random Forest (RF) and XGBoost (XGB)) using engineered time series features. In addition, a convolutional neural network (CNN) was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed 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 the best performing algorithm(s) for exercise classification. Models were evaluated using F1-score in a 10-fold cross validation approach. </sec> <sec> <title>RESULTS</title> The optimal hardware configuration was identified as a 3-sensor setup using lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XGB model achieved the highest exercise (F1=0.94±0.03) and posture (F1=0.90±0.11) classification scores. The CNN achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1=0.94±0.02) and the accelerometer channel alone for posture classification (F1=0.88±0.07). </sec> <sec> <title>CONCLUSIONS</title> This study demonstrates the potential of a 3-sensor lower body wearable solution (e.g. smart pants) that can identify proper sitting postures and exercises in multiple planes, 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. </sec>" @default.
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• W4225137067 date "2022-04-25" @default.
• W4225137067 modified "2023-09-27" @default.
• W4225137067 title "Detecting Low Back Physiotherapy Exercises and Postures: Classifying Inertial Sensor Data with Machine Learning (Preprint)" @default.
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• W4225137067 doi "https://doi.org/10.2196/preprints.38689" @default.
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