Enhancing Home-Based Rehabilitation Exercises with a Temporal Conditional Generative Adversarial Network

Abstract: Physical rehabilitation is essential for restoring motor function; however, traditional methods often require therapist supervision, which can be costly and inaccessible. Home-based rehabilitation offers a practical alternative, but without real-time guidance, patients may develop incorrect movement patterns that impede progress. Existing approaches typically provide feedback only after exercises are completed, reducing their effectiveness. To overcome this limitation, we propose a Temporal Conditional Generative Adversarial Network (TCGAN)-based motion generation system that delivers real-time skeletal guidance tailored to each patient’s body structure and positioning. By detecting key anatomical landmarks and generating adaptive motion sequences, the system ensures precise movement execution, minimizing errors and improving rehabilitation outcomes. Patients can mimic these movements, enabling them to perform exercises with greater accuracy and confidence. Quantitative and qualitative evaluations confirm the effectiveness of the generated exercises, thanks to an optimized architecture, an improved loss function, a refined training process, and fine-tuned TCGAN hyperparameters. Experimental results demonstrate a high degree of similarity between generated and real movements, with a Fréchet Inception Distance (FID) score of 0.89 and strong temporal alignment, as shown by Dynamic Time Warping (DTW) scores ranging from 2.9 to 5.6 across nine rehabilitation exercises. These metrics underscore the system’s realism and reliability.