Abstract

Breast cancer rehabilitation plays a crucial role in the recovery process of post-treatment, emphasizing the significance of effective evaluation systems for rehabilitation exercises. This study explores into the utilization of depth-sensing technologies, particularly focusing on skeleton data, in assessing the efficacy of these exercises. Leveraging the ability of deep learning techniques, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), this study aims to compare their performance in evaluating breast cancer rehabilitation exercises based on skeleton data. The study conducts a comprehensive regression analysis to assess and compare the models' capabilities. The experimental results reveal insights into the comparative effectiveness of CNN and RNN in evaluating the nuances of these exercises, shedding light on their potential applications in enhancing breast cancer rehabilitation evaluation systems.