1,721,090 research outputs found
Myoelectric fatigue profiles during electrically-elicited contractions of vastus lateralis, vastus medialis obliquus, and vastus medialis longus muscles
No full textAccurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric
No full textObjective. A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. Approach. The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. Main results. In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. Significance. The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum
Discharge variability of motor units in the abductor hallucis muscle during electrically-elicited cramps
No full textDischarge properties of motor units in the abductor hallucis muscle during electrically-elicited cramps
No full textElectrical stimulation for neuromuscular testing and training: state-of-the art and unresolved issues
No full textFully Automated Muscle Ultrasound Analysis (MUSA): Robust and Accurate Muscle Thickness Measurement
Full text linkMusculoskeletal ultrasound imaging allows non-invasive measurement of skeletal muscle thickness. Current techniques generally suffer from manual operator dependency, while all the computer-aided approaches are limited to be semi-automatic or specifically optimized for a single muscle. The aim of this study was to develop and validate a fully automatic method, named MUSA (Muscle UltraSound Analysis), for measurement of muscle thickness on longitudinal ultrasound images acquired from different skeletal muscles. The MUSA algorithm was tested on a database of 200 B-mode ultrasound images of rectus femoris, vastus lateralis, tibialis anterior and medial gastrocnemius. The automatic muscle thickness measurements were compared to the manual measurements obtained by three operators. The MUSA algorithm achieved a 100% segmentation success rate, with mean differences between the automatic and manual measurements in the range of 0.06–0.45 mm. MUSA performance was statistically equal to the operators and its measurement accuracy was independent of the muscle thickness value
M-wave properties during progressive motor unit activation by transcutaneous neuromuscular stimulation: effect of the stimulation waveform
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