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Extraction of Multi...
Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth
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- Olsson, Alexander E. (author)
- Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH
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- Sager, Paulina (author)
- Lund University
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- Andersson, Elin (author)
- Lund University
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- Björkman, Anders (author)
- Skåne University Hospital
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- Malešević, Nebojša (author)
- Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH
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- Antfolk, Christian (author)
- Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2019-05-10
- 2019
- English.
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk material- och protesteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Materials (hsv//eng)
Publication and Content Type
- art (subject category)
- ref (subject category)
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