Robotic Rehabilitation for Neurological Conditions through Multilateral Shared Control Architecture

The shortage of therapists required for the rehabilitation of stroke patients, together with the patients’ lack of motivation in regular therapy, builds the need for a robotic rehabilitation platform. While shared control architectures are studied in literature as means of training, the state-of-art training systems involve a complex architecture and moreover have visible performance limitations. In this paper, a simplified training architecture is proposed, which is particularly targeted for rehabilitation, and also adds the missing features such as complete force feedback, enhanced learning rate, and dynamic monitoring of patient’s performance. In addition to the novel architecture, design of controllers to ensure system stability has been presented. These controllers are analytically shown to meet the performance objectives and maintain system’s passivity. An experimental setup is built to test the architecture and the controllers. A comparison with state-of-art methods is also performed to demonstrate the superiority of the proposed method. It is further demonstrated that the proposed architecture facilitates correcting the inaccurate frequencies at which the patient might operate. This was achieved by defining attribute-wise individual recovery factors to the patient.