Machine Learning-Based Hand Gesture Recognition via EMG Data

Basit Öğe Kaydı
dc.contributor.authorKarapinar Senturk, Zehra
dc.contributor.authorBakay, Melahat Sevgul
dc.date.accessioned2021-12-01T18:46:52Z
dc.date.available2021-12-01T18:46:52Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractGestures are one of the most important agents for human-computer interaction. They play a mediator role between human intention and the control of machines. Electromyography (EMG) pattern recognition has been studied for gesture recognition for years to control of prostheses and rehabilitation systems. EMG data gives information about the electrical activity related to muscles. It is obtained from the arm and helps to understand hand gestures. For this work, hand gesture data taken from UCI2019 EMG dataset obtained from myo Thalmic armband were classified with six different machine learning algorithms. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were applied for comparison based on several performance metrics which are accuracy, precision, sensitivity, specificity, classification error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. Splitting ratio in the classification was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in the testing phase of the classifier. NB was found to be the best among other methods because of high accuracy. Classification accuracy varies between 97.52% to 100% for each gesture. Considering the results of the performance metrics, it can be said that this study recognizes and classifies seven hand gestures successfully in comparison with the literature. The proposed method can easily be used for human-machine interaction and smart device controlling like prosthesis, wheelchair, and smart entertainment applications.en_US
dc.identifier.doi10.14201/ADCAIJ2021102123136
dc.identifier.endpage136en_US
dc.identifier.issn2255-2863
dc.identifier.issue2en_US
dc.identifier.startpage123en_US
dc.identifier.urihttps://doi.org/10.14201/ADCAIJ2021102123136
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10024
dc.identifier.volume10en_US
dc.identifier.wosWOS:000704721300002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherEdiciones Univ Salamancaen_US
dc.relation.ispartofAdcaij-Advances In Distributed Computing And Artificial Intelligence Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEMGen_US
dc.subjectmyo armbanden_US
dc.subjecthand gesture recognitionen_US
dc.subjectmachine learningen_US
dc.subjectSchemeen_US
dc.titleMachine Learning-Based Hand Gesture Recognition via EMG Dataen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
Ä°sim:
10021.pdf
Boyut:
1.16 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
Ä°ndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu