Web-based Clinician Guide to Record Compatible Video of Standardized Drinking Task Kinematics for Computer Vision Analysis

Assessment of the upper limb is paramount to the cycle of rehabilitation. Over the last two decades, kinematic assessment of the drinking task activity has been shown to be valid and responsive to clinical change in rehabilitation populations affected by wide-ranging diseases, including stroke and musculoskeletal disease. This foundational work on drinking task kinematics has led to consensus recommendations for the adoption of upper limb kinematics in rehabilitation research. However, practical concerns have been raised, which jeopardize uptake in the research community and translation to clinical practice. Barriers to adoption include the technical complexities of conventional motion capture methods to acquire kinematics and the fidelity of activity protocols behind the kinematics. With regards to the former, breakthroughs in the AI-subfield known as computer vision are drastically reducing the technical burden of kinematic analysis. With regards to the latter, we now present a web-based application (app) designed to guide clinicians through video capture of a highly standardized drinking task activity protocol using an off-the-shelf stereo camera, a personal computer, and basic materials accessible in an outpatient clinical environment. Furthermore, we demonstrate the compatibility of this video capture with a custom-built computer vision pipeline for extracting kinematic metrics of the drinking task. Through ongoing validation and usability studies, we envision that such a tool will significantly improve access to highly standardized upper limb kinematic metrics, which are expected to advance research and clinical care in rehabilitation populations with upper limb impairment.