Computer Vision in Rehabilitation: From Cameras to Better Recovery
Rehabilitation is changing fast, and computer vision is one of the technologies pushing it forward. Computer vision means teaching computers to understand information from images and video, such as how someone moves, how their posture changes, or whether an exercise is being done safely. In rehabilitation, this matters because progress is often measured by movement quality rather than by whether a task was completed.
Why computer vision fits rehabilitation
Many rehab programs depend on observation: clinicians watch movement patterns, count repetitions, and correct technique. Computer vision can support this work by turning everyday video into measurable insights. With appropriate design, vision-based systems can help track joint motion, balance, gait, and functional tasks, providing feedback during therapy and between appointments.
This is especially valuable in telerehabilitation, where patients do exercises at home and clinicians may not see every session. A camera-based system can help maintain consistency—spotting changes in movement over time and flagging potential issues early.
Practical uses today
Computer vision is already being explored in several rehab scenarios:
Exercise form feedback: Systems can detect whether a movement is within a safe range, whether posture is stable, or whether compensations (like leaning or twisting) appear.
Gait and balance monitoring: Video analysis can estimate walking speed, symmetry, step length, and stability—metrics often used in stroke, orthopedic, and aging-related rehab.
Progress tracking: Instead of relying only on occasional clinic tests, vision tools can provide frequent measurements that show trends across weeks.
Assistive robotics and smart devices: Vision can help devices recognize a user’s movement intent or adjust assistance based on real-time motion.
Data quality matters as much as the model
A common misconception is that “better AI” automatically produces better results. In reality, rehabilitation systems are only as reliable as the data they learn from. Movement varies widely across people, diagnoses, environments, and cultures. A high-quality dataset needs to reflect that diversity—different body types, ages, mobility levels, lighting conditions, camera angles, and home settings.
Rehabilitation also has many “edge cases.” For example, someone recovering from stroke may move with subtle compensations that look normal to an algorithm trained only on healthy movement. If those cases are missing from training data, the system can give inaccurate feedback or misjudge progress.
Fairness and accessibility in real homes
Rehab technology must work for people in real-world conditions: small spaces, low lighting, older phones, varied skin tones, mobility aids, and different clothing styles. If a system performs well only in ideal laboratory settings, it can exacerbate health inequities rather than reduce them.
That is why fairness is not optional. It is part of clinical quality. A robust computer vision rehabilitation system should be evaluated across diverse users and environments, and its limitations should be clearly articulated.
What’s next?
Future rehabilitation tools will likely combine multiple sources of information for a clearer picture of recovery. Vision may be paired with wearable sensors, depth cameras, or simple patient-reported measures to improve accuracy and reduce errors. As these systems improve, the goal should remain the same: support clinicians, empower patients, and make high-quality rehabilitation more accessible, without replacing human care.
Citation: Veras, Mirella. (2026, February 01). Computer vision in rehabilitation: From cameras to better recovery. AI for Digital Health Justice. https://www.aireresearch.ca/home/blog/computer-vision1