Movement Quality

Movement quality assessment focuses on objectively measuring the smoothness and efficiency of human movement, primarily to improve rehabilitation outcomes and provide effective feedback in physical therapy. Current research utilizes deep learning architectures, particularly transformer networks and convolutional neural networks, often incorporating attention mechanisms to identify key body parts contributing to movement quality, and employing various smoothness metrics like jerk. These advancements enable automated, scalable assessment of movement, offering valuable insights for clinicians and patients alike, particularly in home-based rehabilitation settings where real-time feedback is crucial.