Despite growing interest in the behavior of electromyographic signals during muscle fatigue, few studies investigate fatigue recovery. In this work, we use surface electromyographic signals to determine the recovery time after isometric fatigue of the biceps brachii muscle in 90° flexion of the non-dominant elbow. Sixty volunteers were arranged into six experimental groups. Experiments were performed in three stages: reference phase (REF), fatigue resistance phase (RES), and recovery phase (REC). An isometric exercise was performed during the RES stage. The time interval between the RES and REC stages was different for each experimental group: 1, 2, 4, 8, 24 and 48 hours. Surface electromyographic signals were acquired during each phase, and the following electromyographic variables were calculated for each phase: median frequency (MDF), root mean squared (RMS) value, and maximum voluntary contraction (MVC). The REF data were compared with the REC data using a paired Wilcoxon test. The results show that the MVC is recovered 2 hours after the exercise. The MDF seems not to be fully recovered after 48 hours, but displays an apparent recovery trend.
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http://dx.doi.org/10.1109/IEMBS.2010.5627256 | DOI Listing |
BMC Neurosci
December 2024
Powell Mansfield, Inc., San Diego, CA, USA.
Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome, not readily available and represents a well-known roadblock to managing this huge societal burden. Assessment of neuromuscular function involved in the upper airway using electromyography (EMG) has shown potential to characterize and diagnose sleep apnea, while the development of transmembranous electromyography (tmEMG), a painless surface probe, has made this opportunity practical and highly feasible.
View Article and Find Full Text PDFJ Neuroeng Rehabil
December 2024
The School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China.
Background: Simultaneous and proportional control (SPC) based on surface electromyographic (sEMG) signals has emerged as a research hotspot in the field of human-machine interaction (HMI). However, the existing continuous motion estimation methods mostly have an average Pearson coefficient (CC) of less than 0.85, while high-precision methods suffer from the problem of long inference time (> 200 ms) and can only estimate SPC of less than 15 hand movements, which limits their applications in HMI.
View Article and Find Full Text PDFFront Neurol
December 2024
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Purpose: This study aims to develop a assessment system for evaluating shoulder joint muscle strength in patients with varying degrees of upper limb injuries post-stroke, using surface electromyographic (sEMG) signals and joint motion data.
Methods: The assessment system includes modules for acquiring muscle electromyography (EMG) signals and joint motion data. The EMG signals from the anterior, middle, and posterior deltoid muscles were collected, filtered, and denoised to extract time-domain features.
J Electromyogr Kinesiol
December 2024
School of Information Science and Technology, Dalian Maritime University, Linghai Road 1, Dalian, Liaoning Province 116026, China. Electronic address:
This study proposed a U-Net based partial convolutional time-domain model for a real-time high-density surface electromyography (HD-sEMG) decomposition. The model combines U-Net and a separation block containing partial convolution, aiming to efficiently identify motor units (MUs) without preprocessing. The proposed U-Net based network was trained by the HD-sEMG signals with innervation pulse trains (IPTs) labels, and the results are compared between different step sizes, noises, and model structures under the sliding time window with 120 sampling points.
View Article and Find Full Text PDFBiomed Eng Online
December 2024
Department of Clinical Physiology, Motion Analysis Center, University Hospital of Toulouse, Hôpital de Purpan, Toulouse, France.
Background: Stroke is the leading cause of acquired motor deficiencies in adults. Restoring prehension abilities is challenging for individuals who have not recovered active hand opening capacities after their rehabilitation. Self-triggered functional electrical stimulation applied to finger extensor muscles to restore grasping abilities in daily life is called grasp neuroprosthesis (GNP) and remains poorly accessible to the post-stroke population.
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