Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study
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 lim...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6459 |
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| author | Swagata Das Wataru Sakoda Priyanka Ramasamy Ramin Tadayon Antonio Vega Ramirez Yuichi Kurita |
| author_facet | Swagata Das Wataru Sakoda Priyanka Ramasamy Ramin Tadayon Antonio Vega Ramirez Yuichi Kurita |
| author_sort | Swagata Das |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:52:17Z |
| format | Article |
| id | doaj.art-9f024cbbda49470d9c5ab891dc10889e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:52:17Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9f024cbbda49470d9c5ab891dc10889e2023-11-22T16:46:26ZengMDPI AGSensors1424-82202021-09-012119645910.3390/s21196459Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility StudySwagata Das0Wataru Sakoda1Priyanka Ramasamy2Ramin Tadayon3Antonio Vega Ramirez4Yuichi Kurita5Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, JapanSchool of Computing, Informatics & Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USAGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, JapanEarly 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.https://www.mdpi.com/1424-8220/21/19/6459artificial neural network (ANN)Random Forest regressorskill assessmentsquatone-leg standinglocomotive syndrome |
| spellingShingle | Swagata Das Wataru Sakoda Priyanka Ramasamy Ramin Tadayon Antonio Vega Ramirez Yuichi Kurita Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study Sensors artificial neural network (ANN) Random Forest regressor skill assessment squat one-leg standing locomotive syndrome |
| title | Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study |
| title_full | Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study |
| title_fullStr | Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study |
| title_full_unstemmed | Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study |
| title_short | Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study |
| title_sort | feature selection and validation of a machine learning based lower limb risk assessment tool a feasibility study |
| topic | artificial neural network (ANN) Random Forest regressor skill assessment squat one-leg standing locomotive syndrome |
| url | https://www.mdpi.com/1424-8220/21/19/6459 |
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