scholarly journals sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm

Life ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 64
Author(s):  
Dongdong Bu ◽  
Shuxiang Guo ◽  
He Li
Keyword(s):  
Upper Limb ◽  
Detection Algorithm ◽  
Joint Movement ◽  
Real Time Control ◽  
Semg Signal ◽  
Motion Recognition ◽  
Upper Limb Rehabilitation ◽  
Upper Limb Exoskeleton ◽  
Time Control ◽  
Exoskeleton Robot

The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the [email protected] could reach 82.3%, and [email protected]–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.

Mirror motion recognition method about upper limb rehabilitation robot based on sEMG

2021 ◽  
pp. 1-9
Author(s):  
Lin Li
Keyword(s):  
Feature Extraction ◽  
Upper Limb ◽  
Limb Movement ◽  
Muscle Synergy ◽  
Rehabilitation Robot ◽  
Semg Signal ◽  
Motion Recognition ◽  
Upper Limb Rehabilitation ◽  
Mirror Motion ◽  
Semg Signals

A novel method of mirror motion recognition by rehabilitation robot with multi-channels sEMG signals is proposed, aiming to help the stroked patients to complete rehabilitation training movement. Firstly the bilateral mirror training is used and the model of muscle synergy with basic sEMG signals is established. Secondly, the constrained L1/2-NMF is used to extracted the main sEMG signals information which can also reduce the limb movement characteristics. Finally the relationship between sEMG signal characteristics and upper limb movement is described by TSSVD-ELM and it is applied to improve the model stability. The validity and feasibility of the proposed strategy are verified by the experiments in this paper, and the rehabilitation robot can move with the mirror upper limb. By comparing the method proposed in this paper with PCA and full-action feature extraction, it is confirmed that convergence speed is faster; the feature extraction accuracy is higher which can be used in rehabilitation robot systems.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Benzhen Guo ◽  
Yanli Ma ◽  
Jingjing Yang ◽  
Zhihui Wang ◽  
Xiao Zhang
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Robotic Arm ◽  
Myoelectric Signal ◽  
Real Time Control ◽  
Myoelectric Signals ◽  
Upper Limb Rehabilitation ◽  
Time Control ◽  
Offline Testing ◽  
Limb Rehabilitation

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.

A NOVEL MULTI-DOF EXOSKELETON ROBOT FOR UPPER LIMB REHABILITATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640023 ◽  
Author(s):  
LIN LIU ◽  
YUN-YONG SHI ◽  
LE XIE
Keyword(s):  
Upper Limb ◽  
Passive Movement ◽  
Joint Movement ◽  
Upper Limb Rehabilitation ◽  
Sitting Posture ◽  
Rehabilitation Training ◽  
Exoskeleton Robot ◽  
Motion Function ◽  
Custom Made ◽  
Limb Rehabilitation

Patients who suffer from stroke have motion function disorders. They need rehabilitation training guided by doctors and trainers. Nowadays, robots have been introduced to help the patients regain their motion function in rehabilitation training. In this paper, a novel multi degree of freedom (DOF) exoskeleton robot, with light weight, including (6[Formula: see text]1) DOFs, named as Rehab-Arm, is proposed and developed for upper limb rehabilitation. The joints of the robot are equipped with micro motors which are capable of actuating each DOF respectively and simultaneously. The medial/lateral rotation of shoulder is realized by a semi-circle guide mechanism for convenience consideration and safety. The robot is used in sitting posture which is attached to a custom made chair. Hence, the robot can be used to assist patients in passive movement with 7 DOFs of the upper limb for rehabilitation. Five adult healthy male subjects participated in the experiment to test the joint movement accuracy of the robot. Finally, subjects can wear Rehab-Arm and move their upper limb, led by micro motors of the robot, to perform task assigned with specific trajectory.

Novel Adaptive Reaching Law for Sliding Mode Control of an Upper Limb Exoskeleton Robot*

2020 ◽  
Author(s):  
Brahim Brahmi ◽  
Khaled El-Monajjed ◽  
Mohammad Habibur Rahman ◽  
Tanvir Ahmed ◽  
Claude El-Bayeh ◽  
...  
Keyword(s):  
Sliding Mode Control ◽  
Upper Limb ◽  
Sliding Mode ◽  
Reaching Law ◽  
Mode Control ◽  
Upper Limb Exoskeleton ◽  
Exoskeleton Robot

Configuration Design and Simulation of Exoskeleton for Upper Limb Rehabilitation Train

2014 ◽  
Vol 701-702 ◽  
pp. 654-658 ◽  
Author(s):  
Yuan Zhang ◽  
Qiang Liu ◽  
Ji Liang Jiang ◽  
Li Yuan Zhang ◽  
Rui Rui Shen
Keyword(s):  
Upper Limb ◽  
Simulation Analysis ◽  
Motion Simulation ◽  
Upper Limb Rehabilitation ◽  
Motion Data ◽  
Upper Limb Exoskeleton ◽  
Movement Data ◽  
Kinematics Simulation ◽  
Motion Process ◽  
Limb Rehabilitation

A new upper limb exoskeleton mechanical structure for rehabilitation train and electric putters were used to drive the upper limb exoskeleton and kinematics simulation was carried. According to the characteristics of upper limb exoskeleton, program control and master - slave control two different ways were presented. Motion simulation analysis had been done by Pro/E Mechanism, the motion data of electric putter and major joints had been extracted. Based on the analysis of the movement data it can effectively guide the electric putter control and analysis upper limb exoskeleton motion process.

Mapping of the Upper Limb Movements and Onset Detection

2013 ◽  
Author(s):  
Alejandra Avila Vasquez ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Upper Limb ◽  
Electrical Potential ◽  
Trapezius Muscle ◽  
Assistive Device ◽  
Real Time Control ◽  
Surface Electrodes ◽  
The Past ◽  
Time Control ◽  
Upper Limb Movements ◽  
Electromyographic Signals

Electromyogram (EMG) consists on the recording and measurement of the electrical potential generated by the activation of muscle fibers [1]. Electromyographic signals (EMGs) are directly linked to the movement performed by a person. Thus, the study of EMGs for the control prosthesis and exoskeletons has become increasingly popular in the past years. To provide a real time control of a prosthesis or exoskeleton (assistive device) to the user, the time between the movement performed by a healthy arm and the movement of the exoskeleton should be small as possible. The main objective of this paper is to map different movements of the upper limb. Moreover, detect the onset of the EMGs to determine which muscle is producing movement. Surface electrodes were used to perform the experiments in order to insure the comfort of the subjects. The analysis of the signal to detect the onset was done using Matlab. After mapping eight movements, results show that the EMGs recorded from the Trapezius muscle can be used as a discriminative to differentiate between movements performed by the arm and movements performed by the forearm and hand. This will reduce the time and number of EMG channels needed to correctly identify the movement performed by the upper limb of a subject.

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