Deep Image Processing for Lower Limb Rehabilitation Training Action and Effect Recognition: GaitSet Algorithm and Full-Field Optical Flow Approaches

Abstract

With advancements in deep learning and image processing technology, evaluations of lower limb rehabilitation training have witnessed improved accuracy and efficiency. However, traditional image processing techniques frequently neglect the inherent sequence-level characteristics during action recognition and fail to exploit the comprehensive full-field data when discerning rehabilitation training action standards. Addressing these limitations, a novel method was proposed. Initially, the GaitSet algorithm was employed to recognize the lower limb rehabilitation training actions, ensuring complete consideration of sequencelevel features. Subsequently, leveraging the full-field optical flow tracking approach, challenges associated with discerning the standards of lower limb rehabilitation training actions were examined. It is anticipated that this novel methodology can offer an enhanced tool for evaluating the effectiveness of lower limb rehabilitation training in the realm of rehabilitation medicine. Such advancements could potentially contribute to optimizing rehabilitation outcomes and augmenting patients' quality of life.