Automated Rehabilitation Exercise Assessment by Genetic Algorithm-optimized CNN

Every year, the number of motor dysfunction patients has been rising. These patients require physical therapy and continuous observation and assessment of their exercises by a professional therapist. This process can take a longer time, leading to a staff shortage and increasing financial costs. Thus, a reliable rehabilitation framework is necessary to assess these exercises as precisely as possible. In this paper, we have proposed an exercise assessment framework using the 1D Local Binary Pattern (LBP) to extract valuable features from skeleton data and a genetic algorithm (GA) optimized Convolutional Neural Network (CNN) to predict the score. The KIMORE dataset has been used in this study. We have achieved 0.0165 Mean Absolute Deviation (MAD) on the training set and 0.13515 on the validation set.