Evaluating Real-World Hand Function after Neurological Injuries

Abstract: The recovery of hand function is a key priority after cervical spinal cord injury (SCI) and stroke. Evaluating hand function is essential to measuring the outcomes of new therapies as well as to supporting clinical care, but observations in the clinic do not fully capture how the hands are being used in a person’s usual home and community environments. Video from wearable cameras (egocentric video) is a form of wearable technology that can provide both detailed information about hand movements as well as valuable contextual information. By leveraging deep learning for the automated analysis of egocentric video, together with detailed consultations with clinicians and individuals living with SCI and stroke, we propose novel solutions for how hand function is evaluated in research and clinical care.