| Title: | Motor unit editing time in high-density EMG decomposition decreases with the operator’s experience and pulse-to-noise ratio but does not depend on simulated muscle |
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| Authors: | ID Murks, Nina, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia (Author)
ID Škarabot, Jakob, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Kramberger, Matej, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia (Author)
ID Sedej, Gašper, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia (Author)
ID Valenčič, Tamara, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Connely, Christopher D., School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Thomason, Haydn, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Divjak, Matjaž, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia (Author)
ID Holobar, Aleš, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia (Author) |
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| Files: |  ERK_2024_Murks.pdf (380,40 KB)
MD5: 962897648943C3CCF55D13F13713F1FC
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.08 - Published Scientific Conference Contribution |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Manual editing of decomposition results represents an important but time-costly aspect of motor unit (MU) identification from a high-density surface electromyogram (hdEMG). We analysed the editing time and number of actions used to manually edit the decomposition results in synthetic signals. We simulated the Biceps Brachii (BB) and Soleus (SO) muscles at four different contraction levels: 10%, 30%, 50%, and 70% of maximum voluntary contraction (MVC). Gaussian noise was added at three signal-to-noise ratios (SNR): 15 dB, 20 dB, and Inf dB. Signals were decomposed into individual MU contributions using the Convolutional Kernel Compensation (CKC) method. The initial Pulse- to-Noise Ratio (PNR) was calculated to estimate automatic MU identification accuracy.
Nine operators with different levels of experience and research backgrounds manually edited the decomposition results. Log files were created to determine the time needed to edit MU, the number of all used actions, and add or delete MU discharge actions.
The initial PNR value of MUs and SNR correlated with editing time, number of all used actions, add actions and delete actions. Contraction level and muscle were not significant factors. The operator’s background and category, on the other hand, influenced the editing time and the number of used actions. |
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| Keywords: | high-density surface electromyography, motor unit, decomposition, biceps brachii, soleus, convolutional
kernel compensation |
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| Publication status: | Not published |
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| Publication version: | Preprint, working version of publication, not peer-reviewed |
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| Year of publishing: | 2024 |
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| Number of pages: | 5 |
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| PID: | 20.500.12556/DKUM-97204  |
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| UDC: | 004.8 |
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| COBISS.SI-ID: | 210704899 |
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| Publication date in DKUM: | 26.02.2026 |
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| Views: | 165 |
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| Downloads: | 2 |
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| Metadata: |    |
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| Categories: | Misc.
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