History of the Study of Skeletal Muscle Function with Emphasis on Kinesiological Electromyography

Blanc, Y.; Dimanico, Ugo

doi:10.2174/1874943701003010084

Cited by 11 publications

(5 citation statements)

References 26 publications

(17 reference statements)

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“…The beginning of investigation into the chemical processes of the brain is attributed to D. Reymond (Du Bois Reymond), who in 1849 demonstrated that the brain, like nerves and muscles, possesses electrogenic properties [1]. However, it was not until 75 years later, in 1924, that the German psychiatrist Hans Berger made the first recording of an electroencephalogram (EEG) [2].…”

Section: Introductionmentioning

confidence: 99%

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Rakhmatulin,

Dao,

Nassibi

et al. 2024

Sensors

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The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.

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Section: Introductionmentioning

confidence: 99%

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Rakhmatulin,

Dao,

Nassibi

et al. 2024

Sensors

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“…The musculoskeletal system is a complex structure made up of bones, muscles, and associated connective tissues that provides stability, movement, and support to the human body, allowing the performance of daily physical activities. It plays a fundamental role in respiratory mechanisms and helps maintain posture, body balance, and equilibrium [ 1 ]. However, several medical conditions can occur due to abnormalities in musculoskeletal function.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensing Techniques with Ultrasound for Musculoskeletal Assessment: A Review

Oliveira

Souza

Assef

et al. 2022

Sensors

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The study of muscle contractions generated by the muscle-tendon unit (MTU) plays a critical role in medical diagnoses, monitoring, rehabilitation, and functional assessments, including the potential for movement prediction modeling used for prosthetic control. Over the last decade, the use of combined traditional techniques to quantify information about the muscle condition that is correlated to neuromuscular electrical activation and the generation of muscle force and vibration has grown. The purpose of this review is to guide the reader to relevant works in different applications of ultrasound imaging in combination with other techniques for the characterization of biological signals. Several research groups have been using multi-sensing systems to carry out specific studies in the health area. We can divide these studies into two categories: human–machine interface (HMI), in which sensors are used to capture critical information to control computerized prostheses and/or robotic actuators, and physiological study, where sensors are used to investigate a hypothesis and/or a clinical diagnosis. In addition, the relevance, challenges, and expectations for future work are discussed.

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“…Examples include the motion capture-based sensors that can capture the kinematic features of humans (Bailey et al, 2018 ), accelerators with dynamic movements and the related metrics (Beato et al, 2019 ), surface electromyography (sEMG) sensors to measure the muscle activities (Edouard et al, 2018 ), etc. Among them, the sEMG signals are shown to have excellent ability on revealing muscle activity at any time instant during various movements and postures (Blanc and Dimanico, 2010 ). The sEMG signal can provide information to characterize muscle fatigue by means of changes in signal indicators (Marshall et al, 2018 ; Toro et al, 2019 ), including the mean absolute value (MAV) (Toro et al, 2019 ), root mean square (RMS) (Girard et al, 2018 ), mean frequency (MNF) and median frequency (MDF) (Bowtell et al, 2014 ), and so on.…”

Section: Introductionmentioning

confidence: 99%

Effective Assessments of a Short-Duration Poor Posture on Upper Limb Muscle Fatigue Before Physical Exercise

Robinson

Tan

et al. 2020

Front. Physiol.

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A forward head and rounded shoulder posture is a poor posture that is widely seen in everyday life. It is known that sitting in such a poor posture with long hours will bring health issues such as muscle pain. However, it is not known whether sitting in this poor posture for a short period of time will affect human activities. This paper investigates the effects of a short-duration poor posture before some typical physical activities such as push-ups. The experiments are set up as follows. Fourteen male subjects are asked to do push-ups until fatigue with two surface electromyography (sEMG) at the upper limb. Two days later, they are asked to sit in this poor posture for 15 min with eight sEMG sensors located at given back muscles. Then they do the push-ups after the short-duration poor posture. The observations from the median frequency of sEMG signals at the upper limb indicate that the short-duration poor posture does affect the fatigue procedure of push-ups. A significant decreasing trend of the performance of push-ups is obtained after sitting in this poor posture. Such effects indicate that some parts of the back muscles indeed get fatigued with only 15 min sitting in this poor posture. By further investigating the time-frequency components of sEMG of back muscles, it is observed that the low and middle frequencies of sEMG signals from the infraspinatus muscle of the dominant side are demonstrated to be more prone to fatigue with the poor posture. Although this study focuses only on push-ups, similar experiments can be arranged for other physical exercises as well. This study provides new insights into the effect of a short-duration poor posture before physical activities. These insights can be used to guide athletes to pay attention to postures before physical activities to improve performance and reduce the risk of injury.

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History of the Study of Skeletal Muscle Function with Emphasis on Kinesiological Electromyography

Cited by 11 publications

References 26 publications

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Multi-Sensing Techniques with Ultrasound for Musculoskeletal Assessment: A Review

Effective Assessments of a Short-Duration Poor Posture on Upper Limb Muscle Fatigue Before Physical Exercise

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