  Timely and adequate rehabilitation is critical in facilitating post-stroke
recovery. However, the organization and delivery of rehabilitation are
resource-demanding, and are only available to approximately 25% of stroke
survivors in low-to-middle-income countries. Improving access to stroke
rehabilitation services through innovative solutions is therefore urgently
required. Tele-rehabilitation, which transits care to home- and community
settings, has emerged as a promising solution. However, current approaches
using video tutorial, teleconference, or other specialized devices face
inherent shortfalls that limit their uptake. In this study, we proposed and
validated the use of an open-source, markerless motion capture model with
consumer-grade devices to overcome these challenges. Our solution enables
reliable measurement of the end range of motion during upper limb exercises
with near-perfect waveform similarity and intraclass correlation to that of the
gold standard Kinect approach. Our multidisciplinary team developed an
automated telerehabilitation framework incorporating the validated markerless
technique to facilitate a seamless telerehabilitation process. It enables
personalized rehabilitation plans with real-time feedback, and individual
progress reports using objective quantitative and qualitative features to
improve patient monitoring and management, and home-based rehabilitation
service uptake and compliance. This study serves as a proof-of-concept in
preparation for the future development of a detailed model of care, and
feasibility, usability, and cost-effectiveness studies of an automated
telerehabilitation platform and framework in improving the state of post-stroke
rehabilitation and functional outcome.
