Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth
In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Scientific reports - 9(2019), 1 vom: 10. Mai, Seite 7244 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Olsson, Alexander E [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 19.10.2020 Date Revised 09.01.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-019-43676-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy | ||
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| 700 | 1 | |a Malešević, Nebojša |e verfasserin |4 aut | |
| 700 | 1 | |a Antfolk, Christian |e verfasserin |4 aut | |
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