Advancing Post-Stroke Recovery and Community Reintegration

Lead  Prof. Kenneth FONG

Department of Rehabilitation Sciences | Research Centre for Assistive Technology | Research Institute for Artificial Intelligence of Things | Research Institute for Smart Ageing

Stroke recovery needs effective rehabilitation to help survivors regain independence and return to everyday life. Prof. Kenneth Fong and his team have pioneered a holistic approach to stroke rehabilitation, developing evidence-based clinical protocols, and innovative rehabilitation technologies that strengthen remote clinical and community care for patients who need seamless and continuous home- and community-based rehabilitation.

Their research has influenced rehabilitation services across hospitals, healthcare organisations, community settings, and generated commercially available solutions such as the patented Remind-to-Move (RTM) wearable device and virtual reality-based cognitive training systems. These innovations have been adopted for a decade by 13 public hospitals in Hong Kong and institutions in the U.S., Singapore, and the Chinese Mainland.

Building on this success, the team recently developed a new-generation close-loop RTM sensory wristband. Paired with a mobile app, the device analyses movement, delivers timely vibration signals to encourage use of the affected limb, and provides real-time feedback and treatment personalized to patients’ disabilities to support rehabilitation in the home and community. The RTM wristband is the world’s first rehabilitation intervention designed to promote use of the hemiparetic arm in adult stroke patients and children with cerebral palsy.

The team also developed the Smart Motion Capture App for Rehabilitation. It uses smartphone cameras for markerless motion capture, automates active range-of-motion measurements, stores longitudinal data and supports standardised motor impairment assessments (e.g., Fugl-Meyer Assessment). A motion mirroring avatar, live joint angle graphs, and laboratory-grade analysis with rendering latency as low as under 10 milliseconds support home exercise, therapist review, and remote assessment, making precision rehabilitation more smart, accessible and convenient.

RAE 2020 Impact Case Study