EMG normalization method based on grade 3 of manual muscle testing: Within- and between-day reliability of normalization tasks and application to gait analysis

Tabard-Fougère, Anne; Rose-Dulcina, Kevin; Pittet, Vincent; Dayer, Romain; Vuillerme, Nicolas; Armand, Stéphane

doi:10.1016/j.gaitpost.2017.10.026

Cited by 26 publications

(24 citation statements)

References 28 publications

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“…Furthermore, we also calculated the linear envelope (LE) of the EMG signal, which is an intuitive processing method of EMG reflecting the muscle force level [56]. Specifically, a full-wave rectification and a lowpass filter with a cutoff frequency of 3 Hz were used to obtain the LE, as previous research has evidenced the ability of the 3 Hz cutoff frequency to provide good relations with muscle force [56][57][58][59][60].…”

Section: Data Pre-processingmentioning

confidence: 99%

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

et al. 2020

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Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular–mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal–Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation.

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Section: Data Pre-processingmentioning

confidence: 99%

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

et al. 2020

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“…Although normalization to an MVC is commonly used, because it is generally repeatable and provides a reference value that can be interpreted easily, i.e., relative to the maximum possible activation of the muscle (Bolgla and Uhl, 2007;Burden, 2010), this is not always possible and may not be the best method for some needed analyses (Hug and Tucker, 2017). Although some methods have been reported as reliable (Albertus-Kajee et al, 2011;Murley et al, 2010;Tabard-Fougere et al, 2018) and give similar values between sessions, this does not ensure that the normalized EMG amplitude value enables a valid comparison of the level of activation of a muscle(s) for a specific application or research question. It is important to consider whether the normalization method is both repeatable and valid, and suitable to answer the specific question being addressed with EMG.…”

Section: Introductionmentioning

confidence: 99%

Consensus for experimental design in electromyography (CEDE) project: Amplitude normalization matrix

Besomi

Hodges

Clancy

et al. 2020

Journal of Electromyography and Kinesiology

193

148

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“…Thus, an improved understanding of the reliability of different EMG variables is warranted. Most studies examining the reliability of EMG variables have been limited to slow, controlled tasks, including gait analysis [12], isometric contractions [13,14] or isokinetic dynamometry of single-joint exercises [15,16], while few studies have analyzed the reliability of EMG variables during functional, dynamic, multi-joint exercises executing the movement at maximal intended velocity [11,[17][18][19]. In these studies, EMG was measured during jump [17][18][19] or squat exercises [11], with reliability scores showing a wide variability depending on the exercise, variable and load magnitude used.…”

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confidence: 99%

Reliability of Mechanical and EMG Variables Assessed During Concentric Bench Press Exercise Against Different Submaximal Loads

Rosell¹

2018

BJSTR

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The assessment of specific neuromuscular performance qualities provides relevant insight into the training status of an individual in relation to the sport requirements. This information is crucial for detecting potential deficiencies in strength and power capacities and, therefore, for designing individualized resistance training programs aimed at improving physical performance [1].Skeletal muscle function can be evaluated using a wide variety of tests, which should provide reliable, valid, sensitive and specific measures to obtain a correct strength diagnosis [2,3]. Therefore, it would seem logical for coaches, strength and physical conditioning professionals and sport scientists to properly assess the reliability of different mechanical and physiological variables related to strength and power qualities [1].

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EMG normalization method based on grade 3 of manual muscle testing: Within- and between-day reliability of normalization tasks and application to gait analysis

Cited by 26 publications

References 28 publications

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

Consensus for experimental design in electromyography (CEDE) project: Amplitude normalization matrix

Reliability of Mechanical and EMG Variables Assessed During Concentric Bench Press Exercise Against Different Submaximal Loads

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