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.
Highlights
The locomotor system of the human body performs complex processes of control and coordination
For selecting the best classifier that is relevant to the proposed input and output dataset, we considered the traditional Machine Learning (ML) methods that were already used for classifying skeletal data in previous research such as, Support Vector Machine (SVM) [39], Random
In addition to Artificial Neural Network (ANN), we considered using the Random Forest (RF) regressor to recognize our skeletal data-based parameters
Summary
The locomotor system of the human body performs complex processes of control and coordination. Our locomotor system’s efficiency is affected by various factors such as accidental injury, aging, arthritis, osteoporosis, and most importantly a sedentary lifestyle. During the COVID-19 pandemic, we have observed an increase in sedentarism or physical inactivity, threatening both physical and mental health [1]. Hospitalization is considered as a contributing factor in causing reduced mobility, resulting in functional decline in older adults during discharge [2]. Reduced physical activity and sedentary behavior predominantly affect older adults due to increased fall risk [3]. The consequences of unexpected falls and related costs constitute substantial concern in the modern world [4]
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