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Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study.
Das, Swagata; Sakoda, Wataru; Ramasamy, Priyanka; Tadayon, Ramin; Ramirez, Antonio Vega; Kurita, Yuichi.
  • Das S; Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, Japan.
  • Sakoda W; Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, Japan.
  • Ramasamy P; Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, Japan.
  • Tadayon R; School of Computing, Informatics & Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA.
  • Ramirez AV; Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, Japan.
  • Kurita Y; Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, Japan.
Sensors (Basel) ; 21(19)2021 Sep 27.
Article in English | MEDLINE | ID: covidwho-1468448
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Machine Learning / Locomotion Type of study: Prognostic study / Randomized controlled trials Language: English Year: 2021 Document Type: Article Affiliation country: S21196459

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Machine Learning / Locomotion Type of study: Prognostic study / Randomized controlled trials Language: English Year: 2021 Document Type: Article Affiliation country: S21196459