scholarly journals sEMG Signal Acquisition Strategy towards Hand FES Control

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Cinthya Lourdes Toledo-Peral ◽  
Josefina Gutiérrez-Martínez ◽  
Jorge Airy Mercado-Gutiérrez ◽  
Ana Isabel Martín-Vignon-Whaley ◽  
Arturo Vera-Hernández ◽  
...  
Keyword(s):  
Electrode Placement ◽  
Signal Acquisition ◽  
Semg Signal ◽  
Motion Patterns ◽  
Upper Limb Rehabilitation ◽  
Hand Motion ◽  
Hand Dexterity ◽  
Severe Fatigue ◽  
Patients Experience ◽  
Limb Rehabilitation

Due to damage of the nervous system, patients experience impediments in their daily life: severe fatigue, tremor or impaired hand dexterity, hemiparesis, or hemiplegia. Surface electromyography (sEMG) signal analysis is used to identify motion; however, standardization of electrode placement and classification of sEMG patterns are major challenges. This paper describes a technique used to acquire sEMG signals for five hand motion patterns from six able-bodied subjects using an array of recording and stimulation electrodes placed on the forearm and its effects over functional electrical stimulation (FES) and volitional sEMG combinations, in order to eventually control a sEMG-driven FES neuroprosthesis for upper limb rehabilitation. A two-part protocol was performed. First, personalized templates to place eight sEMG bipolar channels were designed; with these data, a universal template, called forearm electrode set (FELT), was built. Second, volitional and evoked movements were recorded during FES application. 95% classification accuracy was achieved using two sessions per movement. With the FELT, it was possible to perform FES and sEMG recordings simultaneously. Also, it was possible to extract the volitional and evoked sEMG from the raw signal, which is highly important for closed-loop FES control.

Surface EMG and Upper-Limb Rehabilitation

2011 ◽  
pp. 335-343
Author(s):  
Kazuya Funada ◽  
Jinglong Wu ◽  
Satoshi Takahashi
Keyword(s):  
Experimental System ◽  
Surface Emg ◽  
Passive Movement ◽  
Measuring Device ◽  
Upper Limb Rehabilitation ◽  
Hand Motion ◽  
Characteristic Points ◽  
Opening And Closing ◽  
Limb Rehabilitation ◽  
Simple Measuring

In rehabilitating hemiplegic patients, purposeful movements such as the opening and closing of hands are reported to be more effective than passive movement with an instrument. The authors of this chapter used surface electromyogram (surface EMG) signals as a way to convey the patient’s conscious ability to open and close their hands. The muscles in the forearm contract when the hand is closed or opened, which creates a simple signal that is comparatively easy to measure with surface EMG, a simple measuring device. The action potentials of the muscles involved in the opening-and-closing motions of hands were measured from several points in the forearm when those muscles contracted, and their distribution was analyzed. The purpose of this study is to develop a simple system to recognize the movement of a patient’s hand using measurements of EMG signals from only the most characteristic points on the forearm to replace similar, but more complex, research such as multi-channel measurement and wave analysis by FFT. The authors specified the optimum measuring points on the palm and dorsal sides of the forearm for the recognition of hand motion by the experimental system. This system successfully recognized hand motion through the analysis of the surface EMG signals measured from only two optimum points to allow arbitrary control of the rehabilitation device based on the recognition results.

Motion Synthesis for Upper-Limb Rehabilitation Motion With Clustering-Based Machine Learning Method

2019 ◽  
Author(s):  
Wenxiu Chen ◽  
Wanbing Song ◽  
Haodong Chen ◽  
Qi Li ◽  
Ping Zhao
Keyword(s):  
Machine Learning ◽  
Upper Limb ◽  
Spectral Clustering ◽  
Optimal Solution ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Motion Patterns ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation ◽  
Body Parameters

Abstract Nowadays, mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patient usually has its individual pattern. Thus it is obviously not an optimal solution to use a single motion generator to suit all patients. Yet it would also be unpractical if we design a different motion or even a different mechanism for each user individually. Therefore, in this paper we seek to adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. Firstly, the trajectory of a specified rehabilitation motion are recorded from various subjects, and then 4 types of machine learning algorithms (spectral clustering, hierarchical clustering, self-organizing mapping neural network and Gaussian mixture model) are implemented and compared. It is shown that spectral clustering (SC) yields the best performance and is hereby adopted to generate three clusters of motion patterns. After regression of each cluster, three types of motion for upper limb-rehabilitation are constructed, which could reflect the trajectories’ similarity and difference of people who have various body parameters. These work will provide help for the design of rehabilitation mechanisms.

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

Design of a Single-Degree-of-Freedom Immersive Rehabilitation Device for Clustered Upper-Limb Motion

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Ping Zhao ◽  
Yating Zhang ◽  
Haiwei Guan ◽  
Xueting Deng ◽  
Haodong Chen
Keyword(s):  
Upper Limb ◽  
Daily Basis ◽  
Integrated System ◽  
Degree Of Freedom ◽  
Motion Patterns ◽  
Upper Limb Rehabilitation ◽  
Motion Data ◽  
Mechanical Devices ◽  
Limb Rehabilitation ◽  
Body Parameters

Abstract Mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patients usually has its individual pattern; hence, we adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. By using the regression motion of the clustering result as the target, in this article, we seek to apply kinematic mapping-based motion synthesis framework to design a 1-degree-of-freedom (DOF) mechanism, such that it could lead the patients’ upper limb through the target motion. Also, considering rehab training generally involves a large amount of repetition on a daily basis, this article has developed a rehab system with unity3d based on virtual reality (VR). The proposed device and system could provide an immersive experience to the users, as well as the rehab motion data to the administrative staff for evaluation of users’ status. The construction of the integrated system and the experimental trial of the prototype are presented at the end of this article.

A Multichannel Surface EMG System for Hand Motion Recognition

2015 ◽  
Vol 12 (02) ◽  
pp. 1550011 ◽  
Author(s):  
Yinfeng Fang ◽  
Honghai Liu ◽  
Gongfa Li ◽  
Xiangyang Zhu
Keyword(s):  
Root Mean Square ◽  
Surface Emg ◽  
Signal Acquisition ◽  
Mean Square ◽  
Semg Signal ◽  
Motion Recognition ◽  
Hand Motion ◽  
Prosthetic Hands ◽  
The Relationship ◽  
Semg Signals

Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

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 Sensitivity Based Selection of an Optimal Cable-Driven Parallel Robot Design for Rehabilitation Purposes

Robotics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Ferdaws Ennaiem ◽  
Abdelbadiâ Chaker ◽  
Juan Sebastián Sandoval Arévalo ◽  
Med Amine Laribi ◽  
Sami Bennour ◽  
...  
Keyword(s):  
Pareto Front ◽  
Parallel Robot ◽  
Elastic Stiffness ◽  
Upper Limb Rehabilitation ◽  
Daily Life Activities ◽  
System A ◽  
The Monte Carlo Method ◽  
Genetic Algorithm Method ◽  
Limb Rehabilitation ◽  
Selection Of

This paper deals with the design of an optimal cable-driven parallel robot (CDPR) for upper limb rehabilitation. The robot’s prescribed workspace is identified with the help of an occupational therapist based on three selected daily life activities, which are tracked using a Qualisys motion capture system. A preliminary architecture of the robot is proposed based on the analysis of the tracked trajectories of all the activities. A multi-objective optimization process using the genetic algorithm method is then performed, where the cable tensions and the robot size are selected as the objective functions to be minimized. The cables tensions are bounded between two limits, where the lower limit ensures a positive tension in the cables at all times and the upper limit represents the maximum torque of the motor. A sensitivity analysis is then performed using the Monte Carlo method to yield the optimal design selected out of the non-dominated solutions, forming the obtained Pareto front. The robot with the highest robustness toward the disturbances is identified, and its dexterity and elastic stiffness are calculated to investigate its performance.

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