Lightweight deep neural network models for electromyography signal recognition for prosthetic control

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dc.contributor.authorMert, Ahmet
dc.date.accessioned2026-02-08T15:08:33Z
dc.date.available2026-02-08T15:08:33Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they are formed into appropriate input dimensions, and classified using the LDA, QDA, LDA-CNN, QDA-CNN, LSTM, and CNN. Depending on three prosthetic EMG validation approaches (Scheme 1-3), the accuracy rates of 41.68%, and 47.27% are yielded by LDA, and QDA with 32- dimensional RMS, and WL features while CNN with 2 × 16 input has 82.87% (up to 88.10%). The effect of the learnable filters of the DL approaches, and signal windowing on the success rate and delay time are discussed in the paper. The simulations show that 2D-CNN (accuracy of 82.87% with 1.7 ms delay) can be successfully adapted to prosthetic control devices.
dc.identifier.doi10.55730/1300-0632.4012
dc.identifier.endpage721
dc.identifier.issn1300-0632
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85169686252
dc.identifier.startpage706
dc.identifier.trdizinid1194008
dc.identifier.urihttps://doi.org/10.55730/1300-0632.4012
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5114
dc.identifier.volume31
dc.identifier.wosWOS:001043194400002
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR-Dizin_20260207
dc.subjectDeep learning
dc.subjectconvolutional neural network
dc.subjectHuman-machine interaction
dc.subjectprosthetic hand control
dc.subjectelec- tromyography
dc.titleLightweight deep neural network models for electromyography signal recognition for prosthetic control
dc.typeArticle

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