Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

Abstract

Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition

“…Since the motion intention recognition model applied to the lower limb exoskeleton has high requirements for real-time action, an overly complex model will lead to too long a recognition time, which cannot meet the real-time control of the exoskeleton. Therefore, Guo et al ( 2020 ) uses a lightweight deep learning model, such as a light convolutional neural network (Lw-CNN), to classify and identify the upper arm sEMG that controls the robotic arm, and the robotic arm control accuracy can reach 88.75%. Scheme of adaptive control model based on Lw-CNN is shown in Figure 9 .…”

Explainable Deep Learning Model for EMG-Based Finger Angle Estimation Using Attention

“…The algorithmic (time) complexity of our model is composed of the complexity of the attention mechanism, O(nd), and the complexity of GRU, O(nd 2 ), where n is the number of the EMG sensors and d is the hidden size of the attention matrix and the GRU [24], [42]. According to prior research, the time complexities of machine learning models that control a robot arm using EMG signals, such as a convolutional neural network (CNN) or a multilayer perceptron (MLP) are O(knd 2 ) and O(n), respectively, where n is the number of the sensors, d is the hidden size, and k is the kernel size [43], [44]. We think the time complexity of the proposed model will not be a problem if we select proper n, d and robot hardware.…”

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

“…From the perspective of performing regular activities after limb loss or from the view of people born with congenital defects, arti cial limbs or prostheses are very helpful [1]. Many modern prostheses, such as i-Limb [2], Cyberhand [3], and Yokoi Hand [4], use EMG signals to control multiple degrees of freedom of prosthesis movements since the EMG signal re ects the activity of a muscle corresponding to a movement [5,6]. Electromyography is a technique that senses the bioelectrical potential, also known as the EMG signal, from a target muscle or group of muscles with the help of a surface electrode or needle electrode when these muscles are neurologically activated [7][8][9][10].…”