Home | English | | | UGC | RGC

  A SWOT Analysis of the Research Arena in Hong Kong

  Advanced Micro Robotics in Robot Network and Biomanipulation

  Interactive Control Strategy in a Robotic System for Early Stroke Rehabilitation

  Adaptive robust control for a class of uncertain nonlinear systems and its application to spacecraft control

  An integrated model for monitoring Qinghai-Tibet railway deformation based on DInSAR technology and GPS observations

  Discovery of complex organic matter in space

  Hong Kong – Scotland – Increased understanding on research landscape

The channel selection of the brain signal for stroke rehabilitation training has been featured in the cover story of the top international journal IEEE Transactions on Neural Systems and Rehabilitation Engineering (2011.12)

Stroke is a cerebrovascular accident with high disability and mortality rates. As stated in the Hong Kong Hospital Authority Statistical Report 2005-2010, the number of annual stroke admissions to public hospitals has been increasing from 11,062 cases in 1981 to 25,614 in 2010. It is expected that the burden of stroke, giving rise to difficulties in motor disabilities and affecting the daily life of stroke victims, is likely to escalate substantially in Hong Kong in the future.

Effective motor recovery after stroke depends on intensive voluntary practice of the paretic limbs. However, existing rehabilitation products do not train or enable patients to identify voluntary intention, thus making it difficult for patients to relearn how to reconnect signals and to control their paralyzed limbs with their brains. The new interactive control strategy in a robotic system manages to breakthrough existing limitations. It has been developed for the Brain Training Device, and when used in conjunction with the robotic hand system, it can guide stroke patients to relearn the reconnection between their brains and the paralyzed limbs. Since it can detect brainwaves, it can control the movement of limbs and even a robotic hand.

Voluntary planning and practicing during therapeutic training are keys to successful post-stroke rehabilitation. Electromyography (EMG) and electroencephalography (EEG) are bioelectrical signals generated by the muscle and the cerebral cortex of a brain respectively. These two signals are directly related to the voluntary motor contributions from the central nervous system (EEG, motor imagery, i.e., thinking and planning of a physical task) and from the peripheral neuromuscular system (EMG, motor effort, i.e., execution of a physical task). In this project, we investigated using EMG and EEG for intention-driven robotic training in post-stroke hand-wrist rehabilitation. Our results showed that using 12 EEG electrodes could detect voluntary intention from stroke patients with high accuracy for the 20-session rehabilitation training. The clinical studies also showed the EMG interactive robotic training had better motor functional recovery than passive training on stroke patients after 20 sessions of training. This project developed a new stroke rehabilitation training program by using stroke patient voluntary bioelectrical signals to facilitate better motor function recovery. The findings of this brain control algorithm have been published as the cover story in top international journal IEEE Transactions on Neural Systems and Rehabilitation Engineering (2011.12).
Based on the success of the basic research results, the project had been funded by the HKSAR Government's Innovation and Technology Fund (ITF) in 2011 to implement the control algorithm to build a Brain Training Device with robotic hand for the recovery of survivors after stroke. This novel device can detect brainwave, and thereby control the movement of paralyzed limbs, or go even further to control a robotic hand based on its sophisticated algorithm.

The research was led by Prof. Raymond Tong Kai-yu, who is also the Principal Investigator of the Exoskeleton Hand Robotic Training Device or the “Hand of Hope”, which won the Grand Prix Award in Geneva 2012. The Brain Training Device with Robotic Hand system is able to guide the stroke patients to relearn the reconnection between the brain and the limb. The high accuracy and low number of channels needed mean that the Brain Training Device with Robotic Hand is a viable tool for assistive aid and rehabilitation training. The futuristic system will be made portable and easy-to-use at hospital and home settings. The GRF-funded project had nurtured this new interactive control algorithm for the robotic system using brain and muscle signals. The project has also successfully conducted a translational research to make findings from basic science useful for practical applications that can enhance stroke rehabilitation.

Prof. Raymond Tong (Right) and a stroke subject provided a real-time demonstration of the brain training system on how a stroke patient can control the robot hand with his brainwave and muscle signals on the affected hand.

Prof Raymond Kai-yu Tong
Interdisciplinary Division of
Biomedical Engineering (BME)
The Hong Kong Polytechnic University