Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns

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dc.authorid0000-0002-2917-3736en_US
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorBudak, Ümit
dc.contributor.authorAslan, Muzaffer
dc.contributor.authorAkbulut, Yaman
dc.contributor.authorCömert, Zafer
dc.contributor.authorAkpınar, Muhammed H.
dc.contributor.authorŞengür, Abdulkadir
dc.date.accessioned2021-09-08T07:08:40Z
dc.date.available2021-09-08T07:08:40Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractPhysical action recognition is a hot topic in human-machine interactions. It has potential uses in helping disabled people and in various robotic applications. Electromyography (EMG) signals measure the electrical activity of the muscular systems involved in physical actions. In this chapter, an efficient approach is developed for physical action recognition in humans based on EMG signals. The proposed method is composed of signal decomposition, feature extraction and feature classification. The signal decomposition is carried out using the wavelet packet transform (WPT). The WPT successively decomposes an input signal into its approximation and detail coefficients, which offers a more productive signal analysis. A one-dimensional local binary pattern (LBP) is used to code the approximation and detail coefficients of the decomposed EMG signals. The histogram of the LBP is used as the feature vectors of the EMG physical action classes. The support vector machine (SVM), decision tree, linear discriminant, k-nearest neighbors (k-NN), boosted and bagged tree ensemble classifiers are used in the classification stage. A dataset taken from the UCI machine learning repository is used in the experiments. The Delsys EMG apparatus, which has eight electrodes, is used to record the surface EMG signals. Each class of the dataset contains three male subjects and one female subject. The dataset contains ten physical actions, namely hugging, jumping, bowing, clapping, handshaking, running, sitting, standing, walking and waving. The experiments are carried out on each electrode with a ten-fold cross-validation test, and the average accuracy score is calculated. The experimental results show that the proposed method is quite efficient in EMG signal classification. The calculated average accuracy is 100% for each electrode.en_US
dc.identifier.citationAlçin, Ö. F., Budak, Ü., Aslan, M., Akbulut, Y., Cömert, Z., Akpınar, M. H., & Şengür, A. (2020). Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns. Modelling and Analysis of Active Biopotential Signals in Healthcare, 1, 8-1 - 8-31.en_US
dc.identifier.doi10.1088/978-0-7503-3279-8ch8 8
dc.identifier.endpage8-31en_US
dc.identifier.isbn978-075033279-8
dc.identifier.isbn978-075033277-4
dc.identifier.scopus2-s2.0-85096251919en_US
dc.identifier.startpage8-1en_US
dc.identifier.urihttps://doi.org/10.1088/978-0-7503-3279-8ch8 8
dc.identifier.urihttps://hdl.handle.net/20.500.12899/398
dc.identifier.volume1en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlçin, Ömer Faruk
dc.language.isoenen_US
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofModelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1en_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleClassification of physical actions from surface EMG signals using the wavelet packet transform and local binary patternsen_US
dc.typeBook Chapteren_US

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