A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual reality

Abstract

Motor rehabilitation strategies for treating motor deficits after stroke are based on the understanding of the neural plasticity. In recent years, various upper limb rehabilitation robots have been proposed for the stroke survivors to provide relearning of motor skills by stimulating the motor nerve. However, several aspects including costing, human-robot interaction, and effective stimulation of motor nerve still remain as major issues. In this article, a new upper limb rehabilitation training system named as motion intention-based virtual reality training system is developed to close the aforementioned issues. The system identifies the user's motion intention via force sensors mounted on the rehabilitation robot to conduct therapeutic exercises and stimulates the user's motor nerve by introducing the illusion of immersion in virtual reality environment. The illusion of immersion is developed by creating Virtual Exoskeleton Robot model which is driven by user's motion intention and reflecting the motion states in real time. The users can be present to the training exercises by themselves and fully engage in the virtual reality environment, so that they can relax, move, and recreate motor neuro-pathways. As preliminary phase, six healthy subjects were invited to participate in experiments. The experimental results showed that the motion intention-based virtual reality training system is effective for the upper limb rehabilitation exoskeleton and the evaluations of the developed system showed a significant reduction of the performance error in the training task.

Control Strategy for Power Assist Upper Limb Rehabilitation Robot with the Therapist’s Motion Intention Prediction

“…For this research, only one degree of freedom is used. To overcome some practical challenges in impedance control, 𝐹 𝑔 is the extra gravity compensated force is considered [23]. Eq.…”

Power Assist Rehabilitation Robot and Motion Intention Estimation

“…When the exoskeleton's endpoint is too far away from the blue ball on the prescript trajectory, Xing et al [86] developed an interaction control strategy derived from impedance control to allow the individual to actively execute the training activity while being driven by the power assist robot. The relationship between the interaction torque 𝜏 ℎ of each joint, and the motion state of the endpoint is known as the impedance interaction.…”

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

“…There are nine sets of data under one weight value in one channel, which are labeled as 1 to 9 according to the order stored in the matrix. By the Randi number, the following features of elements in the testing set can be obtained, channel number (1-8), weight (0 kg, 2 kg, 4 kg, and 6 kg), and sequence number (1)(2)(3)(4)(5)(6)(7)(8)(9). The resulting elements from 288 times the process mentioned above are stored in the verification set.…”

“…Some exoskeleton robots with multiple joints in series have been developed for repetitive task training, with unlimited arm workspace in rehabilitation movement and good human-robot interaction performance. 7,8 Virtual reality is often introduced in the procedure to improve training duration and stimulation effect. With the assistance of the exoskeleton, the patient approaches or follows a target through the guidance of the scene.…”