SwagataDas_PlosOne_202102.zip

We propose a feed-forward Artificial Neural Network (ANN)-based lower limb assessment method using squat and one-leg standing exercise features. An Intel Realsense Depth Camera was used to track and record the 3D skeletal data, from which we extracted nine squat features and four one-leg standing features. We used those features as the input layer to train our ANN. We base our assessment method to precisely identify the progression of \textit{Locomotive Syndrome (LS)} in adults. The Japanese Orthopaedic Association (JOA) first defined LS as a mobility disorder that occurs from the degeneration of locomotive organs and makes nursing care mandatory. A self-test called Short Test Battery Locomotive Syndrome (STBLS) was also introduced to identify LS's progression in adults. Therefore, the output layer for training the ANN was obtained using the STBLS test to detect the progression of LS. Three assessment scores were obtained through this test: stand-up, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). The ANN consisted of 2 hidden layers with six nodes per layer. We used the Rectified-Linear-Unit (ReLU) activation function. We observed that the correct choice of the input features to train the ANN was essential to achieve an acceptable risk prediction accuracy. The stand-up and 2-stride scores of the STBLS test could be predicted with correlation coefficients of 0.59 and 0.76 between the real and predicted data, respectively.<br>