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Article

Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study

1
Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, Japan
2
School of Computing, Informatics & Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Irene Cheng and Troy McDaniel
Sensors 2021, 21(19), 6459; https://doi.org/10.3390/s21196459
Received: 19 August 2021 / Revised: 22 September 2021 / Accepted: 24 September 2021 / Published: 27 September 2021
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively. View Full-Text
Keywords: artificial neural network (ANN); Random Forest regressor; skill assessment; squat; one-leg standing; locomotive syndrome artificial neural network (ANN); Random Forest regressor; skill assessment; squat; one-leg standing; locomotive syndrome
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MDPI and ACS Style

Das, S.; Sakoda, W.; Ramasamy, P.; Tadayon, R.; Ramirez, A.V.; Kurita, Y. Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study. Sensors 2021, 21, 6459. https://doi.org/10.3390/s21196459

AMA Style

Das S, Sakoda W, Ramasamy P, Tadayon R, Ramirez AV, Kurita Y. Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study. Sensors. 2021; 21(19):6459. https://doi.org/10.3390/s21196459

Chicago/Turabian Style

Das, Swagata, Wataru Sakoda, Priyanka Ramasamy, Ramin Tadayon, Antonio V. Ramirez, and Yuichi Kurita. 2021. "Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study" Sensors 21, no. 19: 6459. https://doi.org/10.3390/s21196459

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