sEMG-Based Adaptive Cooperative Multi-Mode Control of a Soft Elbow Exoskeleton Using Neural Network Compensation

Soft rehabilitation exoskeletons have gained much attention in recent years, striving to assist the paralyzed individuals restore motor functions. However, it is a challenge to promote human-robot interaction property and satisfy personalized training requirements. This article proposes a soft elbow rehabilitation exoskeleton for the multi-mode training of disabled patients. An adaptive cooperative admittance backstepping control strategy combined with surface electromyography (sEMG)-based joint torque estimation and neural network compensation is developed, which can induce the active participation of patients and guarantee the accomplishment and safety of training. The proposed control scheme can be transformed into four rehabilitation training modes to optimize the cooperative training performance. Experimental studies involving four healthy subjects and four paralyzed subjects are carried out. The average root mean square error and peak error in trajectory tracking test are 3.18 degrees and 5.68 degrees. The active cooperation level can be adjusted via admittance model, ranging from 4.51 degrees /Nm to 10.99 degrees /Nm. In cooperative training test, the average training mode value and effort score of healthy subjects (i.e., 1.58 and 1.50) are lower than those of paralyzed subjects (i.e., 2.42 and 3.38), while the average smoothness score and stability score of healthy subjects (i.e., 3.25 and 3.42) are higher than those of paralyzed subjects (i.e., 1.67 and 1.71). The experimental results verify the superiority of proposed control strategy in improving position control performance and satisfying the training requirements of the patients with different hemiplegia degrees and training objectives.

Automated summary

This article proposes a soft elbow rehabilitation exoskeleton for the multi-mode training of disabled patients. The control strategy, which combines adaptive cooperative admittance backstepping control, sEMG-based joint torque estimation, and neural network compensation, can induce the active participation of patients and ensure the safety and effectiveness of training. Experimental results demonstrate the superiority of the proposed control strategy in improving position control performance and meeting the training requirements of patients with different degrees of hemiplegia.