G-LOC Warning Algorithms Based on EMG Features of the Gastrocnemius Muscle

Kim, Sungho; Cho, Taehwan; Lee, Yongkyun; Koo, Hyojin; Choi, Booyong; Kim, Dong-Soo

doi:10.3357/amhp.4781.2017

Cited by 7 publications

(2 citation statements)

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“…This can result in the rapid shift and pooling of fluid in the lower extremities, away from the brain, which involves the risk of the individual losing consciousness [ 29 ]. Current physiological monitoring to track the risk of G-force-induced loss of consciousness relies upon heart rate monitoring, electroencephalography, or electromyography of the gastrocnemius muscles [ 30 , 31 , 32 ]. These techniques measure a secondary physiological change instead of directly obtaining information regarding the shift in cerebral blood volume, which is the primary mechanism believed to cause G-force-induced loss of consciousness.…”

Section: Discussionmentioning

confidence: 99%

Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications

Griffith

Cluff

Downes

et al. 2023

Sensors

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The alteration of the hydrostatic pressure gradient in the human body has been associated with changes in human physiology, including abnormal blood flow, syncope, and visual impairment. The focus of this study was to evaluate changes in the resonant frequency of a wearable electromagnetic resonant skin patch sensor during simulated physiological changes observed in aerospace applications. Simulated microgravity was induced in eight healthy human participants (n = 8), and the implementation of lower body negative pressure (LBNP) countermeasures was induced in four healthy human participants (n = 4). The average shift in resonant frequency was −13.76 ± 6.49 MHz for simulated microgravity with a shift in intracranial pressure (ICP) of 9.53 ± 1.32 mmHg, and a shift of 8.80 ± 5.2097 MHz for LBNP with a shift in ICP of approximately −5.83 ± 2.76 mmHg. The constructed regression model to explain the variance in shifts in ICP using the shifts in resonant frequency (R2 = 0.97) resulted in a root mean square error of 1.24. This work demonstrates a strong correlation between sensor signal response and shifts in ICP. Furthermore, this study establishes a foundation for future work integrating wearable sensors with alert systems and countermeasure recommendations for pilots and astronauts.

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Section: Discussionmentioning

confidence: 99%

Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications

Griffith

Cluff

Downes

et al. 2023

Sensors

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“…These are the type of features that are extracted from the EMG signal ( ) in the time representation. The most popular features in this domain are wavelength ( ℎ ) (Kim, Cho, Lee, Koo, Choi & Kim, 2017), root mean square ( ) (Cunningham, Plow, Allexandre, Rini & Davis, 2016) and mean absolute value ( ) (Yamanoi, Morishita, Kato & Yokoi, 2015).…”

Section: Figure 3: Emg Signal Feature Domains 121 Time Domain (Td) Featuresmentioning

confidence: 99%

Review of Emg Signal Classification Approaches Based on Various Feature Domains

Jain

Garg

2021

mijst

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Electromyography (EMG) is a widely used analytical practice that relays the health-status of the muscles or the nerve cells by monitoring their electrical impulses. However, it inherits the poor signal-to-noise ratio in addition to occasional signal distortions that significantly challenges the efficacy of this technique. Therefore, since the advent of this technology, numerous researchers have dedicated their study to improve the signal quality by reducing inherent noise in addition to offering its automated classification. In the present work, the authors have presented an overview of various existing researches in the field of electro-myographic signals classification involving various state-of-art techniques. A comprehensive survey has been provided while discussing the EMG signal analytic techniques involving different domains along with their performance. In the process, research not published before 2010 in various authenticated sources, such as Elsevier, PubMed, Springer, IEEE, and other articles and reports that are under the coverage of Web of Science and Google Scholar were analyzed. The review examined the suitability of various existing techniques to empower the healthcare sector based on the

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Surface electromyography using dry polymeric electrodes

Steenbergen,

Busha,

Morgan

et al. 2023

APL Bioengineering

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Conventional wet Ag/AgCl electrodes are widely used in electrocardiography, electromyography (EMG), and electroencephalography (EEG) and are considered the gold standard for biopotential measurements. However, these electrodes require substantial skin preparation, are single use, and cannot be used for continuous monitoring (>24 h). For these reasons, dry electrodes are preferable during surface electromyography (sEMG) due to their convenience, durability, and longevity. Dry conductive elastomers (CEs) combine conductivity, flexibility, and stretchability. In this study, CEs combining poly(3,4-ehtylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) in polyurethane are explored as dry, skin contacting EMG electrodes. This study compares these CE electrodes to commercial wet Ag/AgCl electrodes in five subjects, classifying four movements: open hand, fist, wrist extension, and wrist flexion. Classification accuracy is tested using a backpropagation artificial neural network. The control Ag/AgCl electrodes have a 98.7% classification accuracy, while the dry conductive elastomer electrodes have a classification accuracy of 99.5%. As a conclusion, PEDOT based dry CEs were shown to successfully function as on-skin electrodes for EMG recording, matching the performance of Ag/AgCl electrodes, while addressing the need for minimal skin prep, no gel, and wearable technology.

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G-LOC Warning Algorithms Based on EMG Features of the Gastrocnemius Muscle

Cited by 7 publications

References 0 publications

Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications

Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications

Review of Emg Signal Classification Approaches Based on Various Feature Domains

Surface electromyography using dry polymeric electrodes

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