Efficiency of muscular work is usually measured as the relationship between work load and maximum exercise duration. The present study analyzes the efficiency feature as a ratio between mechanical work (WK) and the energy (E) of the surface electromyographic signal (SEMG). This relation (WK/E(SEMG)) was compared with the most common electromyographic descriptors and its behavior was observed during muscle fatigue. A total of sixteen healthy men (26.8 +/- 4.7 yrs, 175.7 +/- 4.7 cm, and 79.2 +/- 9.4 kg) performed three sets of ten maximal concentric repetitions of dominant knee extension at 60 degrees /s on an isokinetic dynamometer, with 1 minute of rest interval between the sets. The SEMG signals were recorded during the exercises. With the view to minimize the factors other than fatigue that also influence the SEMG descriptors behavior, the only isokinetic repetition phase considered for measurements was the load range. Statistical analyses showed significant correlations between WK/E(SEMG) and the traditional electromyographic fatigue indicators.
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http://dx.doi.org/10.1109/IEMBS.2009.5332581 | DOI Listing |
Sci Data
January 2025
School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
Myoelectric control has emerged as a promising approach for a wide range of applications, including controlling limb prosthetics, teleoperating robots and enabling immersive interactions in the Metaverse. However, the accuracy and robustness of myoelectric control systems are often affected by various factors, including muscle fatigue, perspiration, drifts in electrode positions and changes in arm position. The latter has received less attention despite its significant impact on signal quality and decoding accuracy.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Department of Neuroscience, Northwestern University, 303 East Chicago Ave, Chicago, Illinois, 60611, UNITED STATES.
Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Neurology, National Institute of Medicine of the Ministry of Interior and Administration, 02-507 Warsaw, Poland.
Age-related changes to the orbicularis oculi muscle include impaired eyelid function, such as lagophthalmos, alterations in tear film dynamics, and aesthetic changes like wrinkles, festoons, and the descent of soft tissue. To date, the structural and functional changes that would comprehensively increase our understanding of orbicularis aging have not been analyzed. This study aims to investigate functional outcomes using surface electromyography and correlate them with ultrastructural changes in orbicularis during aging.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions.
View Article and Find Full Text PDFSci Rep
January 2025
Yunnan Diqing Non-Ferrous Metals Co., Ltd, Yunnan, 674400, China.
Fatigue can cause human error, which is the main cause of accidents. In this study, the dynamic fatigue recognition of unmanned electric locomotive operators under high-altitude, cold and low oxygen conditions was studied by combining physiological signals and multi-index information. The characteristic data from the physiological signals (ECG, EMG and EM) of 15 driverless electric locomotive operators were tracked and tested continuously in the field for 2 h, and a dynamic fatigue state evaluation model based on a first-order hidden Markov (HMM) dynamic Bayesian network was established.
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