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.

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 Upper Limb Action Identification Based on Physiological Signals and Its Application in Limb Rehabilitation Training

2021 ◽  
Vol 38 (6) ◽  
pp. 1887-1894
Author(s):  
Chao Zhang ◽  
Ji Zou ◽  
Zhongjing Ma ◽  
Qian Wu ◽  
Zhaogang Sheng ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Upper Limb ◽  
Motor Dysfunction ◽  
Physiological Signals ◽  
Joint Analysis ◽  
Action Identification ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Time Frequency ◽  
Limb Rehabilitation

pper limb motor dysfunction brings huge pain and burden to patients with brain trauma, stroke, and cerebral palsy, as well as their relatives. Physiological signals are closely related to the recovery of patients with limb dysfunction. The joint analysis of two key physiological signals, namely, surface electromyographic (sEMG) signal and acceleration signal, enables the scientific and effective evaluation of upper limb rehabilitation. However, the existing indices of upper limb rehabilitation are incomplete, and the current evaluation approaches are not sufficiently objective or quantifiable. To solve the problems, this paper explores upper limb action identification based on physiological signals, and tries to apply the approach to limb rehabilitation training. Specifically, the upper limb action features during limb rehabilitation training were extracted and identified by time-domain feature method, frequency-domain feature method, time-frequency domain feature method, and entropy feature method. Then, the evaluation flow of upper limb rehabilitation, plus the relevant evaluation indices, were given. Experimental results demonstrate the effectiveness of the proposed composite feature identification of upper limb actions, and the proposed evaluation method for limb rehabilitation.

scholarly journals Using proposed optimization algorithm for solving inverse kinematics of human upper limb applying in rehabilitation robotic

2021 ◽  
Author(s):  
Trung Nguyen ◽  
Tam Bui ◽  
Ha Pham
Keyword(s):  
Upper Limb ◽  
Inverse Kinematics ◽  
Search Space ◽  
Human Motion ◽  
Robot Arm ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Robots ◽  
Inverse Kinematics Problem ◽  
Limb Rehabilitation ◽  
Human Upper Limb

AbstractThe requirement to solve the problem of Inverse Kinetics (IK) plays a very important role in the robotics field in general, and especially in the field of rehabilitation robots, in particular. If the solutions of this problem are not suitable, it can cause undesirable damage to the patient when exercising. Normally, the problem of Inverse Kinematics in the robotics field, as well as the natural field, especially for redundant driven systems, often requires the application of a lot of techniques. The redundancy in Degree of Freedom (DoF), the nonlinearity of the system leads to solve inverse kinematics problem more challenge. In this study, we proposed to apply the self-adaptive control parameters in Differential Evolution with search space improvement (Pro-ISADE) to solve the problem for the human upper limb, which is a very typical redundancy model in nature. First of all, the angles of the joints were measured by a proposed Exoskeleton type Human Motion Capture System (E-HMCS) when the wearer performs some Activities of Daily Living (ADL) and athletic activities. The values of these measured angles joints then were put into the forward kinematics model to find the end effector trajectories. After having these orbits, they were re-fed into the proposed Pro-ISADE algorithm mentioned above to process the IK problem and obtain the predicted joints angular values. The experimental results showed that the predicted joints’ values closely follow the measured joints’ values. That demonstrates the ability to apply the Pro-ISADE algorithm to solve the problem of Inverse Kinetics of the human upper limb as well as the upper limb rehabilitation robot arm.

scholarly journals Human Posture Detection Method Based on Wearable Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoou Li ◽  
Zhiyong Zhou ◽  
Jiajia Wu ◽  
Yichao Xiong
Keyword(s):  
Standard Deviation ◽  
Upper Limb ◽  
Plantar Pressure ◽  
Gait Recognition ◽  
Human Motion ◽  
Lower Limbs ◽  
Identification Accuracy ◽  
Support Vector ◽  
Human Posture ◽  
Semg Signals

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.

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 Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Main Trend ◽  
Motor Rehabilitation ◽  
Rehabilitation Process ◽  
Upper Limb Rehabilitation ◽  
Multiple Sensors ◽  
Meta Analyses ◽  
Limb Rehabilitation ◽  
And Control

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

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