GAit Rehabilitation Exoskeleton

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Gait disorder is a common lasting side-effect for stroke and spinal cord injury survivors. It has been recognised that task specific repetitive training and patients’ active participation can lead to more effective gait rehabilitation. It is important to ensure the robotic gait rehabilitation training should be more analogous to actual human walking and maximize the training subject's active participation. The goal of this work is the development of a new robotic GAit Rehabilitation Exoskeleton (GAREX) that is compliant with the current neurorehabilitation theories in order to achieve optimised robotic gait rehabilitation. Such goal is tackled systematically in terms of both robotic design and control algorithm research.


The ReachHab is the appropriate choice for both hospital and home use. Different with other low-cost devices in the markets, it can provide an assistant force in the recovery process. It makes severe patients can take passive rehabilitation exercises under totally device leading and minor stroke patients take active rehabilitation exercises by their own intention. Some effective recovery tasks are achieved by analysing the conventional physical exercise for stroke. The research of human-machine interaction is conducted to reveal a method in interaction tasks design. Different recovery patterns are compared in this article to select the appropriate training model in upper limb rehabilitation. The approach built in this research offers a potential inspiration in upper limb rehabilitation device development. This device has been further improved for bilateral rehabilitation use in clinics, and clinical trials have been conducted on a number of stroke patients in the Jiaxing Hospital, China. 


Control strategies are the key to implement the training theories into robotic operations. In order to encourage patients' active participation, the robot should be controlled to supply just enough guidance/assistance a patient needs to complete the training tasks. To implement assist-as-needed (AAN) concept, the robot needs to be able to assess the extent of active participation and changing the assistance provided accordingly. The intrinsic compliance of GAREX’s PM actuation system could be utilized to change the level of guidance. A new multi-input-multi-output (MIMO) sliding model (SM) controller was developed to simultaneously control the angular trajectory and compliance of GAREX. To online assess the training patient’s active participation, a fuzzy logic compliance adaptation (FLCA) controller is proposed. The FLCA algorithm utilizes the robotic kinematic and human-exoskeleton interaction torque of the knee joint, to estimate the extent of the patient’s active participation. Based on the estimation, the desired compliance level can be automatically adjusted with higher compliance for more active participation and vice versa.

On the other hand, post-stroke depression (PSD) has been considered the most frequent neuropsychiatric consequence of stroke. In future work, we also will develop a new treatment strategy for PSD by integrating a virtual-reality game into a robot-assisted rehabilitation system for treating PSD. Its clinical efficacy will be monitored using a quantitative assessment technique based on electroencephalograms (EEGs), and will be also qualitatively evaluated. Functionally, the therapist can easily operate this system through a user-friendly interface to customise the training strategy for an individual.