Recent evidence suggests different regions of the rectus femoris (RF) muscle respond differently to squat exercises. Such differential adaptation may result from neural inputs distributed locally within RF, as previously reported for isometric contractions, walking and in response to fatigue. Here we therefore investigate whether myoelectric activity distributes evenly within RF during squat. Surface electromyograms (EMGs) were sampled proximally and distally from RF with arrays of electrodes, while thirteen healthy volunteers performed 10 consecutive squats with 20% and 40% of their body weight. The root mean square (RMS) value, computed separately for thirds of the concentric and eccentric phases, was considered to assess the proximo-distal changes in EMG amplitude during squat. The channels with variations in EMG amplitude during squat associated with shifts in the muscle innervation zone were excluded from analysis. No significant differences were observed between RF regions when considering squat phases and knee joint angles individually (P>0.16) while a significant interaction between phase and knee joint angle with detection site was observed (P<0.005). For the two loads considered, proximal RMS values were greater during the eccentric phase and for the more flexed knee joint position (P<0.001). Our results suggest inferences on the degree of RF activation during squat must be made cautiously from surface EMGs. Of more practical relevance, there may be a potential for the differential adaption of RF proximal and distal regions to squat exercises.
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http://dx.doi.org/10.1016/j.jelekin.2017.01.003 | DOI Listing |
Life (Basel)
November 2024
Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
After a fracture, patients have reduced willingness to bend and extend their elbow joint due to pain, resulting in muscle atrophy, contracture, and stiffness around the elbow. Moreover, this may lead to progressive atrophy of the muscles around the elbow, resulting in permanent functional loss. Currently, a goniometer is used to measure the range of motion, ROM, to evaluate the recovery of the affected limb.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Exero Medical Ltd., Or Yehuda 6037606, Israel.
Anastomotic leakage (AL) is one of the most devastating complications after colorectal surgery. The verification of the adequate perfusion of the anastomosis is essential to ensuring anastomosis integrity following colonic resections. This study aimed to evaluate the efficacy of measuring the electrical activity of the colonic muscularis externa at an anastomosis site for perfusion analysis following colorectal surgery.
View Article and Find Full Text PDFNeurogastroenterol Motil
January 2025
Division of Gastroenterology, School of Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Background: Gastric dysmotility and gastric slow wave dysrhythmias have been well documented in patients with diabetes. However, little is known on the effect of hyperglycemia on small intestine motility, such as intestinal slow waves, due to limited options in measuring its activity. Moreover, food intake and digestion process have been reported to alter the small intestine motility in normal rats, but their roles in that of diabetic rats remains unknown.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Electrical and Computer Engineering Department, University of New Brunswick, 3 Bailey Dr., Fredericton, New Brunswick, E3B5A3, CANADA.
Objective: While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human-machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
School of Materials Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture recognition due to their powerful automatic feature extraction capabilities. sEMG signals contain rich local details and global patterns, but single-scale convolutional networks are limited in their ability to capture both comprehensively, which restricts model performance.
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