An adaptation strategy of using LDA classifier for EMG pattern recognition

Zhang, Haoshi; Zhao, Yingliang; Yao, Fuan; Xu, Lisheng; Shang, Peng; Li, Guanglin

doi:10.1109/embc.2013.6610488

Cited by 40 publications

(12 citation statements)

References 14 publications

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“…The recorded FSR data were classified using the linear discriminant analysis (LDA) provided by MATLAB software from MathWorks. LDA was chosen for this study because of its ease to apply it in real-time, and ability to achieve similar or better classification results than other more complex methods (Englehart and Hudgins, 2003 ; Scheme and Englehart, 2011 ; Zhang et al, 2013 ; Amsuss et al, 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study

Cho

Chen

Merhi

et al. 2016

Front. Bioeng. Biotechnol.

111

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Advancement in assistive technology has led to the commercial availability of multi-dexterous robotic prostheses for the upper extremity. The relatively low performance of the currently used techniques to detect the intention of the user to control such advanced robotic prostheses, however, limits their use. This article explores the use of force myography (FMG) as a potential alternative to the well-established surface electromyography. Specifically, the use of FMG to control different grips of a commercially available robotic hand, Bebionic3, is investigated. Four male transradially amputated subjects participated in the study, and a protocol was developed to assess the prediction accuracy of 11 grips. Different combinations of grips were examined, ranging from 6 up to 11 grips. The results indicate that it is possible to classify six primary grips important in activities of daily living using FMG with an accuracy of above 70% in the residual limb. Additional strategies to increase classification accuracy, such as using the available modes on the Bebionic3, allowed results to improve up to 88.83 and 89.00% for opposed thumb and non-opposed thumb modes, respectively.

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Section: Methodsmentioning

confidence: 99%

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study

Cho

Chen

Merhi

et al. 2016

Front. Bioeng. Biotechnol.

111

View full text Add to dashboard Cite

show abstract

“…After the feature selection, an N-dimensional feature vector was formed for each sample, and there were 90 samples for each tested arm. A conventional linear discriminant classifier (LDC) was used for classification between normality and injury ( Smola et al, 2000 ; Webb, 2003 ; Zhang H. et al, 2013 ) because of its satisfactory performance and high practicability for surface EMG classification ( Englehart et al, 1999 ). The implementation of the LDC is to construct a linear classifier by modeling the within-class density of each class as a multi-variant Gaussian distribution ( Mika et al, 1999 ).…”

Section: Methodsmentioning

confidence: 99%

Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography

Tang

Zhang

Yong

et al. 2020

Front. Bioeng. Biotechnol.

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Background: There is a great demand for convenient and quantitative assessment of upper-limb traumatic peripheral nerve injuries (PNIs) beyond their clinical routine. This would contribute to improved PNI management and rehabilitation. Objective: The aim of this study was to develop a novel surface EMG examination method for quantitatively evaluating traumatic upper-limb PNIs. Methods: Experiments were conducted to collect surface EMG data from forearm muscles on both sides of seven male subjects during their performance of eight designated hand and wrist motion tasks. All participants were clinically diagnosed as unilateral traumatic upper-limb PNIs on the ulnar nerve, median nerve, or radial nerve. Ten healthy control participants were also enrolled in the study. A novel framework consisting of two modules was also proposed for data analysis. One module was first used to identify whether a PNI occurs on a tested forearm using a machine learning algorithm by extracting and classifying features from surface EMG data. The second module was then used to quantitatively evaluate the degree of injury on three individual nerves on the examined arm. Results: The evaluation scores yielded by the proposed method were highly consistent with the clinical assessment decisions for three nerves of all 34 examined arms (7 × 2 + 10 × 2), with a sensitivity of 81.82%, specificity of 98.90%, and significate linear correlation (p < 0.05) in quantitative decision points between the proposed method and the routine clinical approach. Conclusion: This study offers a useful tool for PNI assessment and helps to promote extensive clinical applications of surface EMG.

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“…LDA is a robust machine learning algorithm that finds a linear combination of features to separate two or more classes of objects. Additionally, LDA has been identified as to have real-time applicability and provides better or equal classification results when compared to more complex algorithms (E. Scheme and Englehart 2011; Scheme et al 2013;Zhang et al 2013;Amsuss et al 2014). For Trials 1 and 2, Leave-One-Out Cross-Validation (LOOCV) method was used to obtain the classification accuracy.…”

Section: Machine Learning Algorithmmentioning

confidence: 98%

A Multi-sensor Approach for Biomimetic Control of a Robotic Prosthetic Hand

Ghataurah

Ferigo

Merhi

et al. 2017

Bioinformatics and Biomedical Engineering

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Abstract. Robotic prosthetic hands with five digits have become commercially available however their use is limited to a few grip patterns due to the unnatural and unreliable human-machine interface (HMI). The research community has addressed this problem extensively by investigating Pattern Recognition (PR) based surface-electromyography (sEMG) control. This control strategy has been recently commercialized however has yet to show clinical adoption. One of the reasons identified in the literature is due to the sEMG signals that are affected by sweating, electrode shift, ambient noise, fatigue, cross-talk between adjacent muscles, signal drifting, and force level variation. Hence recently the scientific community has started proposing multi-modal sensing techniques as a solution.This study aims to investigate the use of multi-modal sensor approach to control a robotic prosthetic hand by investigating the sparsely studied sensing mechanism called Force Myography (FMG) as a synergist to the conventional technique of sEMG. FMG uses pressure sensors on the surface of a limb to detect the volumetric changes in the underlying musculotendinous complex. This paper presents a custom prosthetic prototype instrumented with sEMG and FMG sensors and tested by a participant with a transradial amputation. Results demonstrate that this multi-sensor approach has the potential to be a valid HMI for prosthesis control.

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An adaptation strategy of using LDA classifier for EMG pattern recognition

Cited by 40 publications

References 14 publications

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study

Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography

A Multi-sensor Approach for Biomimetic Control of a Robotic Prosthetic Hand

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