Surface Electromyography to Study Muscle Coordination

Hug, François; Tucker, Kylie

doi:10.1007/978-3-319-14418-4_184

Cited by 5 publications

(4 citation statements)

References 63 publications

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“…For example, it has been proposed that normalization of EMG amplitude in a standardized submaximal task enables comparison between groups when an MVC is not possible. Unfortunately, the likelihood that participants in the groups perform the normalizing task in a manner that differs between groups, it renders this form of analysis invalid (Hug & Tucker, 2017). If this method is used, this limitation upon the interpretation of data should be considered and discussed.…”

Section: Discussionmentioning

confidence: 99%

“…Various normalization methods have been described (Burden, 2010;Merlo and Campanini, 2016), such as the reference to a maximal voluntary contraction (MVC), sub-maximal voluntary contraction, peak/ mean during task, etc. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Besomi

Hodges

Clancy

et al. 2020

Journal of Electromyography and Kinesiology

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210

148

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Section: Discussionmentioning

confidence: 99%

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

Self Cite

210

148

View full text Add to dashboard Cite

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“…The writing speed being unaffected by the pencil grip force and muscular activity holds promise for the application of these cues in the remedial program for disabled persons. Surface Electromyography (sEMG) has proven out to study muscle coordination [110], which can be exploited to understand the poor muscle tone in motor dysgraphia. sEMG signals generated during handwriting have been already applied for character recognition [111,112] and Parkinson's disease diagnosis [113].…”

Section: New Framework For Diagnosis and Assistance Evaluation Of Dys...mentioning

confidence: 99%

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

Kunhoth,

Al-Maadeed,

Kunhoth

et al. 2024

IJDAR

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Learning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.

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“…However, assessment of facial expressions through muscle activation patterns poses challenges for studies of deception. The recording technology must have high resolution and muscle specificity (Hug & Tucker, 2018;Van Boxtel, 2010), while keeping the participants comfortable. Indeed, contemporary facial sEMG devices are cumbersome, unstable, prone to noise, record only for a limited time, rely on expert placement, and produce signals with low spatial resolution (Hug & Tucker, 2018;Wolf, 2015).…”

mentioning

confidence: 99%

Lie to my face: An electromyography approach to the study of deceptive behavior

et al. 2021

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Background: Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in reallife scenarios remain elusive.Methods: By integrating electrophysiological and communicative approaches, we demonstrate a new and objective detection approach to identify participant-specific indicators of deceptive behavior in an interactive scenario of a two-person deception task. We recorded participants' facial muscle activity using novel dry screen-printed electrode arrays and applied machine-learning algorithms to identify lies based on brief facial responses.Results: With an average accuracy of 73%, we identified two groups of participants:Those who revealed their lies by activating their cheek muscles and those who activated their eyebrows. We found that the participants lied more often with time, with some switching their telltale muscle groups. Moreover, while the automated classifier, reported here, outperformed untrained human detectors, their performance was correlated, suggesting reliance on shared features. Conclusions:Our findings demonstrate the feasibility of using wearable electrode arrays in detecting human lies in a social setting and set the stage for future research on individual differences in deception expression.

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Surface Electromyography to Study Muscle Coordination

Cited by 5 publications

References 63 publications

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

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

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

Lie to my face: An electromyography approach to the study of deceptive behavior

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