Techniques of EMG signal analysis: detection, processing, classification and applications (Correction)

Reaz, Mamun Bin Ibne; Hussain, M. Sazzad; Mohd-Yasin, F.

doi:10.1251/bpo124

Cited by 99 publications

(52 citation statements)

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“…Electromyography (EMG) has been assessed by non-invasive surface electrodes and invasive intramuscular electrodes [72]. Surface EMG places the electrodes directly on the skin and therefore the signal is a combination of all the muscle fibre action potentials occurring in the muscles underlying the skin electrodes [72].…”

Section: Methodsmentioning

confidence: 99%

Importance and challenges of measuring intrinsic foot muscle strength

Soysa

Hiller

Refshauge

et al. 2012

Journal of Foot and Ankle Research

119

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BackgroundIntrinsic foot muscle weakness has been implicated in a range of foot deformities and disorders. However, to establish a relationship between intrinsic muscle weakness and foot pathology, an objective measure of intrinsic muscle strength is needed. The aim of this review was to provide an overview of the anatomy and role of intrinsic foot muscles, implications of intrinsic weakness and evaluate the different methods used to measure intrinsic foot muscle strength.MethodLiterature was sourced from database searches of MEDLINE, PubMed, SCOPUS, Cochrane Library, PEDro and CINAHL up to June 2012.ResultsThere is no widely accepted method of measuring intrinsic foot muscle strength. Methods to estimate toe flexor muscle strength include the paper grip test, plantar pressure, toe dynamometry, and the intrinsic positive test. Hand-held dynamometry has excellent interrater and intrarater reliability and limits toe curling, which is an action hypothesised to activate extrinsic toe flexor muscles. However, it is unclear whether any method can actually isolate intrinsic muscle strength. Also most methods measure only toe flexor strength and other actions such as toe extension and abduction have not been adequately assessed. Indirect methods to investigate intrinsic muscle structure and performance include CT, ultrasonography, MRI, EMG, and muscle biopsy. Indirect methods often discriminate between intrinsic and extrinsic muscles, but lack the ability to measure muscle force.ConclusionsThere are many challenges to accurately measure intrinsic muscle strength in isolation. Most studies have measured toe flexor strength as a surrogate measure of intrinsic muscle strength. Hand-held dynamometry appears to be a promising method of estimating intrinsic muscle strength. However, the contribution of extrinsic muscles cannot be excluded from toe flexor strength measurement. Future research should clarify the relative contribution of intrinsic and extrinsic muscles during intrinsic foot muscle strength testing.

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Section: Methodsmentioning

confidence: 99%

Importance and challenges of measuring intrinsic foot muscle strength

Soysa

Hiller

Refshauge

et al. 2012

Journal of Foot and Ankle Research

119

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“…WGN [10][11]. Examples of filters are band-pass filter and band-stop filter [5][6]. Adaptive filter [12], wavelet denoising algorithm [13], and advance digital signal filter [14] have been devised for WGN.…”

Section: Noise Reduction In Emg Signalmentioning

confidence: 99%

Multiple classifier system with sensitivity based dynamic weighting fusion for hand gesture recognition

Huang

Chan

Zhou

et al. 2016

2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

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Surface electromyogram (sEMG) signal has been applied to gesture recognition successfully. However, performance of gesture recognition degrades due to noise, which is unavoidable in practical environment. This study aims to propose a gesture recognition system using a multiple classifier system (MCS) based on random subspace. Our method makes a decision using a signal which is less sensitive to noise. Each base classifier is trained using the subset of channels selected randomly. The influence of a base classifier by a noisy sample is measured by sensitivity, defined as the perturbation of an output of a classifier. The final decision of the MCS is determined according to the output of the based stable classifiers fused by majority vote. Experimental results show that the proposed method achieves better classification performance in comparison with its base classifier and the random subspace method in terms of accuracy and robustness to noise.

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“…4), close to each other, in a muscle group, e.g., on the biceps, indicates the force generated. It is necessary the placing of a third electrode as a signal reference [15,16]. Since the EMG signal crosses different tissues to reach the electrodes on the skin surface, the signal acquired will contain noise, which makes its measurement a non-straightforward acquisition.…”

Section: ) Electromyogram (Emg) Acquisitionmentioning

confidence: 99%

Remote physiological data acquisition: From the human body to electromechanical simulators

Barros

Leão

Soares

et al. 2013

2013 2nd Experiment@ International Conference (exp.at'13)

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The problems that health professionals faced may involve engineering practicalities, e.g., the design of efficient devices in physiological specific monitoring activities. Therefore, it is important to know the type and characteristics of the physiological signals and how to acquire them. This paper presents the conceptual model for physiological data acquisition in a new remote didactic environment. This comprises two different approaches: human physiological and electromechanical simulators data acquisition. The insights on the data acquisition system development are described, emphasizing the added value of this new innovative educational tool for biomedical engineering.

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Techniques of EMG signal analysis: detection, processing, classification and applications (Correction)

Cited by 99 publications

References 0 publications

Importance and challenges of measuring intrinsic foot muscle strength

Importance and challenges of measuring intrinsic foot muscle strength

Multiple classifier system with sensitivity based dynamic weighting fusion for hand gesture recognition

Remote physiological data acquisition: From the human body to electromechanical simulators

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