RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

Abstract

Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

“… The generation, processing, and application of FMG, EMG, and EIT signals. ( a ) Use FMG to predict forces in two directions [ 12 ]; ( b ) a novel kirigami-based bracelet senses the skin impedance signals, which is used to distinguish between different gestures [ 13 ]; ( c ) identify the movement intention based on sEMG [ 14 ]; ( d ) an EIT-based technique for assessing spinal cord injury [ 15 ]. …”

“…The generation, processing, and application of FMG, EMG, and EIT signals are showed in Figure 1. to predict forces in two directions [12]; (b) a novel kirigami-based bracelet senses the skin impedance signals, which is used to distinguish between different gestures [13]; (c) identify the movement intention based on sEMG [14]; (d) an EIT-based technique for assessing spinal cord injury [15]. [12]; (b) a novel kirigami-based bracelet senses the skin impedance signals, which is used to distinguish between different gestures [13]; (c) identify the movement intention based on sEMG [14]; (d) an EIT-based technique for assessing spinal cord injury [15].…”

Adaptive Neural & Fuzzy Controller for Exoskeleton Gait Pattern Control Based on Musculoskeletal Modeling

“…Figure 1 shows the sequence of steps required to design rehabilitation robots. The RBF neural network can be used to evaluate the unknown nonlinearities in the system in accordance with the adaptive laws [8]. The RBF Network is fused with the adaptive SMC controller to provide with the improved approximation performance of the system.…”

“…In another work [8] the author has used surface electromyography(sEMG) technique to predict the joint angles for upper limb rehabilitation. Then the Adaptive SMC controller utilizing radial basis network was designed to control the upper limb movement while per forming rehabilitation.…”

“…where Λ is a positive symmetric matrix and Λ = Λ T > 0 From eq.6 we can write q as: q = −s + qdes + Λe sys (8) Then calculate Iṡ…”