Dynamic muscle fatigue detection using self-organizing maps

Moshou, Dimitrios; Hostens, Ivo; Papaioannou, George; Ramón, Herman

doi:10.1016/j.asoc.2004.09.001

Cited by 45 publications

(27 citation statements)

References 13 publications

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“…The shift of dominant frequencies towards the lower frequency band is with respect to time and hence in the present study wavelet transforms was used to analyze sEMG. Our finding is in concurrence to an observation made by Moshou et al (2005). They made this observation on a study, which involved analysis of fatigue in trapezius muscle of five subjects who drove for one hour in a simulated environment.…”

Section: Article In Presssupporting

confidence: 94%

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EMG-based analysis of change in muscle activity during simulated driving

Balasubramanian

Adalarasu

2007

Journal of Bodywork and Movement Therapies

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Section: Article In Presssupporting

confidence: 94%

“…Amplitude of the approximation coefficients follows closely the muscle fatigue development (Moshou et al, 2005). EMG signal was analyzed off-line using MATLAB 6p5 software (Mathworks, Version 6.1.0.450 Release 12.1).…”

Section: Data Processingmentioning

confidence: 99%

EMG-based analysis of change in muscle activity during simulated driving

Balasubramanian

Adalarasu

2007

Journal of Bodywork and Movement Therapies

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“…The amplitude of approximation coefficients coincide with muscle fatigue development. Moshou et al (2005) proposed a method for automating the detection of muscle fatigue by using NNs, where a two-dimensional self-organising map visualises the approximation of wavelet coefficients, enabling the visualisation of the onset of fatigue over time, and thus separating the EMG signal from fresh and fatigued muscles. Tepavac & Schwirtlich (1997) developed a technique which utilises the processed sEMG signal as an activation signal that changes the pattern to control a functional electrical stimulation (FES) system.…”

Section: Application Of Emg In Muscle Fatigue Researchmentioning

confidence: 99%

sEMG Techniques to Detect and Predict Localised Muscle Fatigue

Almulla¹,

Sepúlveda²,

Colley³

2012

EMG Methods for Evaluating Muscle and Nerve Function

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“…Artificial neural networks have been used to detect muscle activity by Moshoua et al (Moshoua, Hostensa & Papaioanno 2005). In that work, wavelet coefficients were proposed as features for identifying muscle fatigue.…”

Section: Introductionmentioning

confidence: 99%

Detecting Localised Muscle Fatigue During Isometric Contraction Using Genetic Programming

Kattan

Almulla

Sepúlveda

et al. 2009

Proceedings of the International Joint Conference on Computational Intelligence

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We propose the use of Genetic Programming (GP) to generate new features to predict localised muscles fatigue from pre-filtered surface EMG signals. In a training phase, GP evolves programs with multiple components. One component analyses statistical features extracted from EMG to divide the signals into blocks. The blocks' labels are decided based on the number of zero crossings. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is applied to group similar data blocks. Each cluster is then labelled into one of three types (Fatigue, Transition-to-Fatigue and Non-Fatigue) according to the dominant label among its members. Once a program is evolved that achieves good classification, it can be used on unseen signals without requiring any further evolution. During normal operation the data are again divided into blocks by the first component of the program. The blocks are again projected onto a two-dimensional Euclidean space by the two other components of the program. Finally blocks are labelled according to the k-nearest neighbours. The system alerts the user of possible approaching fatigue once it detects a Transition-to-Fatigue. In experimentation with the proposed technique, the system provides very encouraging results.

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Dynamic muscle fatigue detection using self-organizing maps

Cited by 45 publications

References 13 publications

EMG-based analysis of change in muscle activity during simulated driving

EMG-based analysis of change in muscle activity during simulated driving

sEMG Techniques to Detect and Predict Localised Muscle Fatigue

Detecting Localised Muscle Fatigue During Isometric Contraction Using Genetic Programming

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