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Decoding of Multipl...
Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier
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- D'Accolti, Daniele (författare)
- Scuola Superiore Sant'Anna di studi universitari e di perfezionamento (SSSUP),Sant'Anna School of Advanced Studies (SSSUP)
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- Dejanovic, Katarina (författare)
- Scuola Superiore Sant'Anna di studi universitari e di perfezionamento (SSSUP),Sant'Anna School of Advanced Studies (SSSUP)
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- Cappello, Leonardo (författare)
- Scuola Superiore Sant'Anna di studi universitari e di perfezionamento (SSSUP),Sant'Anna School of Advanced Studies (SSSUP)
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- Mastinu, Enzo, 1987 (författare)
- Scuola Superiore Sant'Anna di studi universitari e di perfezionamento (SSSUP),Sant'Anna School of Advanced Studies (SSSUP)
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- Ortiz Catalan, Max Jair, 1982 (författare)
- Göteborgs universitet,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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- Cipriani, C. (författare)
- Scuola Superiore Sant'Anna di studi universitari e di perfezionamento (SSSUP),Sant'Anna School of Advanced Studies (SSSUP)
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 31, s. 208-217
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the \textit {transient} EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of 89%. Importantly, for each amputee, it produced at least one \textit {acceptable} combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to 80%), they were not as good with amputees (accuracy up to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Annan medicin och hälsovetenskap -- Övrig annan medicin och hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Other Medical and Health Sciences -- Other Medical and Health Sciences not elsewhere specified (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- transient EMG
- pattern recognition
- hand wrist prosthetics
- cross-subject classifier
- Myoelectric control
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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