scholarly journals Wrist and Finger Gesture Recognition With Single-Element Ultrasound Signals: A Comparison With Single-Channel Surface Electromyogram

2019 ◽  
Vol 66 (5) ◽  
pp. 1277-1284 ◽  
Author(s):  
Jiayuan He ◽  
Henry Luo ◽  
Jie Jia ◽  
John T. W. Yeow ◽  
Ning Jiang
Keyword(s):  
Gesture Recognition ◽  
Single Channel ◽  
Single Element ◽  
Surface Electromyogram ◽  
Channel Surface ◽  
Ultrasound Signals

scholarly journals Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2022
Author(s):  
Luca Mesin
Keyword(s):  
Single Channel ◽  
Motor Units ◽  
Single Channels ◽  
Surface Electromyogram ◽  
Non Invasive ◽  
Parallel Fibres ◽  
Channel Surface ◽  
Large Phase ◽  
Cross Correlations

Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference).

Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier

2018 ◽  
Vol 46 ◽  
pp. 121-130 ◽  
Author(s):  
Mahmoud Tavakoli ◽  
Carlo Benussi ◽  
Pedro Alhais Lopes ◽  
Luis Bica Osorio ◽  
Anibal T. de Almeida
Keyword(s):  
Gesture Recognition ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Channel Surface ◽  
Double Channel

Individual Finger Control of Prosthetic Hand from Weak and Single-Channel Surface EMG

2020 ◽  
Author(s):  
John Jairo Villarejo Mayor ◽  
Anselmo Frizera Neto ◽  
André Luiz Félix Rodacki ◽  
Teodiano Freire Bastos-Filho
Keyword(s):  
Single Channel ◽  
Surface Emg ◽  
Prosthetic Hand ◽  
Individual Finger ◽  
Channel Surface ◽  
Finger Control

Differentiation of Epileptic and Psychogenic Nonepileptic Seizures Using Single-Channel Surface Electromyography

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Aatif M. Husain ◽  
Alan R. Towne ◽  
David K. Chen ◽  
Luke E. Whitmire ◽  
Shannon R. Voyles ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Single Channel ◽  
Psychogenic Nonepileptic Seizures ◽  
Channel Surface ◽  
Nonepileptic Seizures
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