A Computational Model Based Study Of Supervised Haptics-enabled Therapist-in-the-Loop Training For Upper-Limb Post-Stroke Robotic Rehabilitation
This paper proposes a new framework for neural-network-based supervised training of intensity and strategy for upper-limb haptics-enabled robotic neurorehabilitation systems for post-stroke motor disabilities. Two alternative approaches are implemented: (a) Haptics-enabled Teleoperated Supervised Training (HTST); and (b) EMG-based Indirect Supervised Training (EIST). The design of both techniques includes two phases: (a) characterizing and learning the therapeutic intensity and strategy when a therapist delivers robotics-assisted rehabilitation to a patient (demonstration phase), and (b) enabling regeneration of the learned therapeutic behavior when the therapist is out of the loop, e.g., when she/he is working with another patient (regeneration phase). For the first phase, HTST platform allows for direct transformation of the forces generated by the therapist to deliver rehabilitation at the patient side, and providing the therapist with direct force feedback. In contrast, EIST is an indirect platform which utilizes the posture of the therapist for generation of rehabilitation forces. EIST uses vibration to the therapist's arm to make the therapist aware of the forces applied to the patient's hand. Although HTST is a more intuitive alternative, EIST is safer, portable, wearable, less expensive, and provides relative motion freedom for the therapist. The proposed training framework is motivated by the existing challenge regarding the need for tuning the strategy and intensity of robotic rehabilitation systems in a patient-specific manner. It also enables therapists to share their time between several patients. Experimental results are presented to evaluate the engineering aspects of the work and feasibility of the concept, where a computational model is used to simulate motor disability of a post-stroke patient.