Robotic Rehabilitation 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, creates the need for a robotic rehabilitation platform. While studies on shared control architectures are present in the literature as a means of training, state-of-the-art training systems involve a complex architecture and, moreover, have notable performance limitations. In this paper, a simplified training architecture is proposed that is particularly targeted for rehabilitation and also adds missing features, such as complete force feedback, enhanced learning rate, and dynamic monitoring of the patient’s performance. In addition to the novel architecture, the design of controllers to ensure system stability is presented. These controllers are analytically shown to meet the performance objectives and maintain the system’s passivity. An experimental setup is built to test the architecture and the controllers. A comparison with the state-of-the-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 for the patient.