1. Motor unit identification in the M waves recorded by high-density electromyMiloš Kalc, Jakob Škarabot, Matjaž Divjak, Filip Urh, Matej Kramberger, Matjaž Vogrin, Aleš Holobar, 2023, izvirni znanstveni članek Ključne besede: M wave, high-density surface EMG, firing identification, motor unit
Objavljeno v DKUM: 13.06.2024; Ogledov: 144; Prenosov: 15
 Celotno besedilo (2,30 MB)
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2. Identification of motor unit firings in H-reflex of soleus muscle recorded by high-density surface electromyographyMiloš Kalc, Jakob Škarabot, Matjaž Divjak, Filip Urh, Matej Kramberger, Matjaž Vogrin, Aleš Holobar, 2023, izvirni znanstveni članek Ključne besede: motor units identification, high-density surface EMG, decomposition, H-reflex
Objavljeno v DKUM: 13.06.2024; Ogledov: 133; Prenosov: 15
Celotno besedilo (1,43 MB)
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3. Materials for HybridNeuro webinar titled "Validation of results: statistical models and MU identification accuracy"Aleš Holobar, Nina Murks, 2024, zaključena znanstvena zbirka raziskovalnih podatkov Opis: This dataset contains a collection of teaching materials that were used in the HybridNeuro project webinar titled "Validation of results: statistical models and MU identification accuracy". The webinar was presented by Aleš Holobar and covered the complexities of motor unit (MU) identification accuracy, regression analysis and Bayesian models. The primary aim of the webinar was to spark a robust discussion within the scientific community, particularly focusing on the application and implications of linear mixed models and Bayesian regression in the realm of MU identification. The teaching materials include Matlab and R source code for statistical analysis of the included data, as well as three examples of MU identification results in CSV format (from both synthetic and experimental HDEMG signals). The presentation slides in PDF format are also included. The dataset is approximately 9 MB in size.
Ključne besede: HybridNeuro, webinar, teaching materials, statistical models, regression analysis, motor unit identification, matlab, rstudio, statistics, surface high density electromyogram (HDEMG), tibialis anterior, dataset
Objavljeno v DKUM: 30.05.2024; Ogledov: 221; Prenosov: 25
Celotno besedilo (108,35 KB)
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4. Simulated and experimental HDEMG signals of biceps brachii muscle for analysis of motor unit mergingAleš Holobar, Jakob Škarabot, Dario Farina, 2024, zaključena znanstvena zbirka raziskovalnih podatkov Opis: This dataset contains a collection of simulated and experimental surface HDEMG recordings of the biceps brachii muscle during the isometric elbow flexion. Simulated data contains 50 recordings: 5 subjects and 5 excitation levels, each with and without added noise. Experimental data contains 16 recordings: 2 subjects with 4 excitation levels and 2 repetitions of each level. Synthetic data was simulated using the cylindrical volume conductor model [1] and the motor unit recruitment and firing modulation model proposed in [2]. Each recording is 20 seconds in length with 90 HDEMG channels sampled at 2048 Hz and is stored as a 2D matrix of raw EMG values in Matlab’s MAT format. Experimental surface EMG data was recorded on two volunteers during isometric contractions at constant force level. Each recording is 25 seconds in length with 64 HDEMG channels sampled at 2048 Hz and is also stored as a 2D matrix of raw EMG values in Matlab’s MAT format. The dataset is approximately 1.5 GB in size.
Ključne besede: surface high density electromyogram (HDEMG), motor unit, spike train, motor unit merging, simulated data, experimental data, biceps brachii, dataset
Objavljeno v DKUM: 30.05.2024; Ogledov: 172; Prenosov: 23
Celotno besedilo (125,29 KB)
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7. Influence of the milling strategy on the durability of forging toolsIvo Pahole, Dejan Studenčnik, Karl Gotlih, Mirko Ficko, Jože Balič, 2011, izvirni znanstveni članek Opis: The quality of a tool's surface has a direct influence on the number of well-produced parts. For the machining of an active tool surface, two technological processes are used: electrical discharge machining and high-speed milling. These two processes are used when machining new tools and for the repairing of used forging tools. In both cases, the material has already been thermally treated, so it has to be used for hard milling. Practical experience shows that the milling strategy has a big influence on the durability of a forging tool. This paper shows the influence of the CNC machining direction during high-speed milling on the durability of the engraving within the forging tool. In some cases the correct milling strategy can increase the durability of the forging tool by about one third.
Ključne besede: orodja za kovanje, kakovost površine, visokohitrostno rezkanje, CNC-rezkanje, forging tools, surface quality, high speed cutting, CNC milling
Objavljeno v DKUM: 10.07.2015; Ogledov: 1530; Prenosov: 121
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8. Real-time motor unit identification from high-density surface EMGVojko Glaser, Aleš Holobar, Damjan Zazula, 2013, izvirni znanstveni članek Opis: This study addresses online decomposition of high-density surface electromyograms (EMG) in real-time. The proposed method is based on previouslypublished Convolution Kernel Compensation (CKC) technique and sharesthe same decomposition paradigm, i.e. compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ~ 10 s of EMG signal, the real-time implementation begins with batch processing of ~ 3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal Linear Minimum Mean Square Error (LMMSE) estimator demonstrates high agreementin identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98 %. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90 % of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.
Ključne besede: discharge pattern, high-density EMG, surface EMG, motor unit, real time decomposition
Objavljeno v DKUM: 25.05.2015; Ogledov: 1639; Prenosov: 0 |
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