Electrotactile Feedback in a Virtual Hand Rehabilitation Platform: Evaluation and Implementation

Abstract

Tactile feedback plays an important role in hand manipulation, especially in the grasping process which is one of the major functions of the hand. However, few commercially available prosthetic hands or hand motor function rehabilitation systems are equipped with tactile feedback. The absence of suitable tactile feedback modules leads to an inferior rehabilitation performance with a large burden on user training and compromised usability. Thus, it is challenging and essential to integrate a proper tactile feedback module with the existing hand rehabilitation systems to achieve a better control performance and accelerate the rehabilitation process. This paper focuses on the implementation and evaluation of the electrotactile feedback (EF) enhanced rehabilitation system. A virtual hand rehabilitation platform is proposed comprising an surface electromyography (sEMG) acquisition module, an electrotactile stimulation module, a virtual environment with sEMG-driven humanlike hand and numerical feedbacks of grasping force and fingertip deformation, where a closed-loop control is formed. Three different feedback conditions including visual feedback (VF), EF, and no feedback (NF) are compared based on the proposed platform. Experiments were conducted on 10 able-bodied subjects, and multiple quantitative metrics for the rehabilitation performance evaluation including training burden estimation and success rate (SR) of tasks were adopted. Results indicate that the integration of EF is helpful to both reduce the rehabilitation duration and improve the virtual grasping SR in comparison with the NF condition while possessing a better practicality over VF. Note to Practitioners —This paper is motivated by the problem of hand grasp control for rehabilitation purposes, but it also applies to other hand motor function rehabilitation process. Existing hand motor function rehabilitation approaches generally lack a proper feedback and rely on the heavy burden during user training and the users experience. This paper suggests incorporating EF to improve the efficiency and efficacy of the rehabilitation process. The electrical stimulation is driven by the myoelectric-sensing-based force estimation and encoded in a manual scheme to fit each individual involved. In our work, a virtual hand rehabilitation platform is implemented to verify the feasibility of EF in reducing the burden of user training and improving the rehabilitation performance, which allows the expanding of the current system into a broader spectrum of motor function rehabilitation applications. Experiments on able-bodied subjects suggest that the EF in the proposed virtual hand rehabilitation platform is feasible, but it has not been tested on the limb-impaired subjects and confined to a manual encoding of electrical stimulation. In the future research, the design of a general EF enhanced hand rehabilitation platform with a standardized stimulation parameter optimization will be addressed and further validated on the subjects with limb impairments and amputation.