A comparison of EMG-based muscle fatigue assessments during dynamic contractions

Rogers, D.R.; MacIsaac, Dawn

doi:10.1016/j.jelekin.2013.05.005

Cited by 52 publications

(23 citation statements)

References 16 publications

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“…The EMG signals recorded in isotonic conditions are widely used in clinical applications to classify neuromuscular diseases [22] and muscles fatigue [23]. Meanwhile, a large and growing body of literature analyzes EMG signals in isometric and isotonic contractions to control assistive robots, lower-limb orthoses and exoskeletons as reviewed in [24,25].…”

Section: Motivationmentioning

confidence: 99%

“…They modified the mean and median frequency by calculating the mean and median of amplitude spectrum instead of power spectrum, which are defined as modified mean frequency (MMNF) and modified median frequency (MMDF). The MNF, median frequency (MDF), bandwidth (BW) and Normalized spectral moments (NSM) are extracted to detect muscle fatigue for upper limbs [23]. Farina and Merletti defines the PSD of a wide sense stationary stochastic process as a Fourier transform of the autocorrelation function of the EMG signal [33].…”

Section: Automated Emg Analysismentioning

confidence: 99%

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A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Nazmi

Rahman

Yamamoto

et al. 2016

Sensors

302

177

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In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

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Section: Motivationmentioning

confidence: 99%

Section: Automated Emg Analysismentioning

confidence: 99%

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Nazmi

Rahman

Yamamoto

et al. 2016

Sensors

302

177

View full text Add to dashboard Cite

show abstract

“…According to Lorenzoni [32], electromyography has been used for the clinical assessment of individuals with suspected metabolic myopathy ever since the first description of MS in 1951. Indeed, this method is practical, and simple, and plays a relevant role in the diagnosis and follow-up of conditions that lead to fatigue of the muscle tissue [61]. An analysis of the results of studies on motor nerve conduction following short-term maximal muscular effort or repeated stimulation may reveal findings typical of MS.…”

Section: Neural (Neuromuscular) Implicationsmentioning

confidence: 99%

Neuromuscular, cognitive and metabolic implications of McArdle Syndrome: a global overview

Vieira

Soares

Felipe

et al. 2015

IJBAS

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Carbohydrates are the main source of body energy and they can be stored in the organism in form of glycogen and degraded when there is a need for energy. However, McArdle Syndrome patients exhibit problems to degrade glycogen due to a deficiency in myophosphorylase enzyme, making these patients intolerant to high intensity exercises because a lack of ATP available in muscle cells. It has been found muscular weakness and subsarcolemmal accumulation of glycogen in muscle fibers and in neuronal cells in McArdle Syndrome patients. In its later-onset form, it is associated to muscular dysmorphia, myoglobinuria and rhabdomyolisis. Analyzing the biochemical aspects, it is possible to notice that these patients have a low rate of ATP production due to a reduction in the availability of glucose, reducing oxidative phosphorylation. However, the metabolic "second wind" effect allows the use of other energy sources. Excessively decreased exercise-induced lactate is also characteristic of patients with McArdle Syndrome. Electromyography studies describe alterations in nerve conduction and the necessity of recruiting more motor units to contract muscle in these patients. McArdle Syndrome produces several metabolic changes in patients due to absence of myophophorylase activity. The practice of aerobic exercise acts positively in these patients probably by increasing mitochondrial metabolism.

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“…The onset of muscle fatigue results in a decrease in conduction velocity, thereby pushing the spectrum towards lower frequencies (De Luca 1984). Thus, as contraction intensity increases, two different phenomena, successive recruitment and muscle fatigue, would act on spectral indexes in opposite directions (Tenan et al 2011;Gorostiaga et al 2012;Rogers and MacIsaac 2013).…”

Section: Effects Of Muscle Architecturementioning

confidence: 99%

Influence of inter-electrode distance, contraction type, and muscle on the relationship between the sEMG power spectrum and contraction force

2014

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A comparison of EMG-based muscle fatigue assessments during dynamic contractions

Cited by 52 publications

References 16 publications

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Neuromuscular, cognitive and metabolic implications of McArdle Syndrome: a global overview

Influence of inter-electrode distance, contraction type, and muscle on the relationship between the sEMG power spectrum and contraction force

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