Feature fusion-based hand gesture classification with time-domain descriptors and multi-level deep attention network

Basit Öğe Kaydı
dc.contributor.authorAlcin, Omer Faruk
dc.contributor.authorKorkmaz, Deniz
dc.contributor.authorAcikgoz, Hakan
dc.date.accessioned2026-04-04T13:35:12Z
dc.date.available2026-04-04T13:35:12Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractIn conventional human-robot interaction (HRI), it is difficult to provide adaptability by located systems in the human body. Surface Electromyography (sEMG) signals have the potential to meet adaptability in HRI by directly representing movements, and classifying hand gestures with sEMG can be an effective solution to meet the increasing needs of these applications. In this paper, a hybrid and multi-scale convolutional neural network (CNN) model is proposed to obtain an efficient sEMG-based classification approach of human hand gestures. The proposed method includes an effective feature extraction process, including spectral moments, sparseness, irregularity factor, Teager-Kaiser energy, Shannon entropy, Katz fractal dimension, and Higuchi's fractal dimension, and waveform length. The obtained features are then converted to RGB images. The designed network is built on multi-scale convolutional blocks with residual learning and convolutional blocks, including the CBAM to improve the network performance by focusing on channel and spatial features. Furthermore, a pyramid non-pooling local block is utilized at the end of the network to learn more powerful features and their correlations. Five comprehensive publicly available datasets are evaluated in the experiments, and the obtained results are compared with the benchmark CNN models and network variations with different attention mechanisms. In the comparative evaluations, the CBAM achieves a classification accuracy between 84.62 % and 97.56 % while other attention mechanism results give accuracy values between 82.88 % and 97.17 %. The experiments show that the proposed method gives more accurate and robust classification performance compared with other variations and benchmark models.
dc.identifier.doi10.1016/j.asoc.2025.113375
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.orcid0000-0002-6432-7243
dc.identifier.orcid0000-0002-5159-0659
dc.identifier.orcid0000-0002-2917-3736
dc.identifier.scopus2-s2.0-105006677860
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2025.113375
dc.identifier.urihttps://hdl.handle.net/11616/109696
dc.identifier.volume178
dc.identifier.wosWOS:001501108600006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectHand gesture classification
dc.subjectSEMG
dc.subjectAttention mechanism
dc.subjectConvolutional neural network
dc.subjectHuman-robot interaction
dc.titleFeature fusion-based hand gesture classification with time-domain descriptors and multi-level deep attention network
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu