Computer vision-based rehabilitation techniques require transition from engineering parameters to Evidence-Based Medicine reasoningComputer Vision Systems Offer New Paths for Physical Rehabilitation

The digital transformation of rehabilitation training has become a public health imperative driven by a global demand that outstrips professional medical resources and is compounded by a deficit in public rehabilitation literacy. As traditional hospital-centric models reach their scalability limits, there is a critical necessity for accessible home-based care solutions to ensure patients do not miss optimal recovery windows. To evaluate computer vision as a potential solution, this paper conducts a systematic review following the PRISMA 2020 reporting framework. We identify that its clinical migration faces a profound “Paradigm Gap” across three critical domains which this study aims to address: (1) the Perception Domain, where algorithms are constrained by inherent reconstruction ambiguities and pathological data scarcity; (2) the Assessment Domain, where a “semantic gap” persists between low-level features and clinical reasoning; and (3) the Coaching Domain, where feedback mechanisms fail to translate summative “Knowledge of Results (KR)” into actionable “Knowledge of Performance (KP).” We formulate and adopt “Perception, Assessment, and Coaching (PAC)” as a novel taxonomy to serve as a logical grid for systematically analyzing existing literature and elucidating technical pathways required to resolve these clinical challenges. This review synthesizes the technological landscape into three evolutionary trajectories: (1) In Perception, research is shifting toward constructing biomechanically consistent digital twins to eliminate visual hallucinations. (2) In Assessment, frontier methods are establishing interpretable clinical reasoning engines to achieve a leap from engineering parameters to Evidence-Based Medicine (EBM) evidence. (3) In Coaching, focus lies in precise movement correction via semantic translation and multimodal strategies to support motor relearning. Furthermore, we explore the potential of Generative AI and Multimodal Large Language Models (MLLMs) in reshaping interaction paradigms (e.g., Visual Self-Modeling) alongside critical discussions on ethical boundaries. Through a systematic literature review, this paper elucidates the task boundaries of rehabilitation vision and constructs the PAC taxonomy. It provides a robust theoretical roadmap and forward-looking guidance for the design of the next generation of clinically valid intelligent rehabilitation systems.