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 Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5385
Author(s):  
Tianyang Zhong ◽  
Donglin Li ◽  
Jianhui Wang ◽  
Jiacan Xu ◽  
Zida An ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Human Motion ◽  
Identification Accuracy ◽  
Classification Model ◽  
Upper Limb Rehabilitation ◽  
Time Frequency ◽  
Limb Rehabilitation ◽  
Semg Signals

Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on upper-limb motions is lost in the process of feature extraction for sEMG signals, and there exists the fact that only a small variety of rehabilitation movements can be distinguished, and the classification accuracy is easily affected. To solve these dilemmas, first, a multiscale time–frequency information fusion representation method (MTFIFR) is proposed to obtain the time–frequency features of multichannel sEMG signals. Then, this paper designs the multiple feature fusion network (MFFN), which aims at strengthening the ability of feature extraction. Finally, a deep belief network (DBN) was introduced as the classification model of the MFFN to boost the generalization performance for more types of upper-limb movements. In the experiments, 12 kinds of upper-limb rehabilitation actions were recognized utilizing four sEMG sensors. The maximum identification accuracy was 86.10% and the average classification accuracy of the proposed MFFN was 73.49%, indicating that the time–frequency representation approach combined with the MFFN is superior to the traditional machine learning and convolutional neural network.

Extraction of sEMG Signal in Upper Limb Rehabilitation Robot

2017 ◽  
Vol 11 (3) ◽  
pp. 729-735
Author(s):  
Jiahan Li ◽  
Gongfa Li ◽  
Disi Chen ◽  
Weiliang Ding ◽  
Jianyi Kong ◽  
...  
Keyword(s):  
Upper Limb ◽  
Rehabilitation Robot ◽  
Semg Signal ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

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%.

Upper-limb Rehabilitation Robot Based on Motor Imagery EEG

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Baoguo XU ◽  
Si PENG ◽  
Aiguo SONG
Keyword(s):  
Motor Imagery ◽  
Upper Limb ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

Upper-limb Rehabilitation Robot Motion Control Based on Dynamic Interpolation

ROBOT ◽  
2012 ◽  
Vol 34 (5) ◽  
pp. 539 ◽  
Author(s):  
Lizheng PAN ◽  
Aiguo SONG ◽  
Guozheng XU ◽  
Huijun LI ◽  
Baoguo XU
Keyword(s):  
Motion Control ◽  
Upper Limb ◽  
Robot Motion ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

scholarly journals A scoping review of design requirements for a home-based upper limb rehabilitation robot for stroke

2021 ◽  
pp. 1-15
Author(s):  
Lutong Li ◽  
Qiang Fu ◽  
Sarah Tyson ◽  
Nick Preston ◽  
Andrew Weightman
Keyword(s):  
Upper Limb ◽  
Scoping Review ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Home Based ◽  
Design Requirements ◽  
Limb Rehabilitation
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