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Electromyography-Based Sign Language Recognition: A Low-Channel Approach for Classifying Fruit Name Gestures

Kudratjon ZohirovSoftware and Hardware Support of Computer Systems, Karshi State Technical University, Karshi 180100, UzbekistanMirjakhon TemirovConvergence of Digital Technologies, Tashkent University of Information Technologies, Tashkent 100100, UzbekistanSardor BoykobilovSoftware and Hardware Support of Computer Systems, Karshi State Technical University, Karshi 180100, UzbekistanGolib BerdievSoftware and Hardware Support of Computer Systems, Karshi State Technical University, Karshi 180100, UzbekistanFeruz RuziboevConvergence of Digital Technologies, Tashkent University of Information Technologies, Tashkent 100100, UzbekistanKhojiakbar EgamberdievComputer Systems, University of Economics and Pedagogy, Karshi 180100, UzbekistanMamadiyor SattorovSoftware and Hardware Support of Computer Systems, Karshi State Technical University, Karshi 180100, UzbekistanGulmira PardayevaInformation Technology, University of Information Technology and Management, Karshi 180100, UzbekistanKuvonch MadatovComputer Systems, University of Economics and Pedagogy, Karshi 180100, Uzbekistan

Signalsjournal2025en

ABI

Annotatsiya

This paper presents a method for recognizing sign language gestures corresponding to fruit names using electromyography (EMG) signals. The proposed system focuses on classification using a limited number of EMG channels, aiming to reduce classification process complexity while maintaining high recognition accuracy. The dataset (DS) contains EMG signal data of 46 hearing-impaired people and descriptions of fruit names, including apple, pear, apricot, nut, cherry, and raspberry, in sign language (SL). Based on the presented DS, gesture movements were classified using five different classification algorithms—Random Forest, k-Nearest Neighbors, Logistic Regression, Support Vector Machine, and neural networks—and the algorithm that gives the best result for gesture movements was determined. The best classification result was obtained during recognition of the word cherry based on the RF algorithm, and 97% accuracy was achieved.

Mavzular

Hand Gesture Recognition Systems Hearing Impairment and Communication Tactile and Sensory Interactions

Identifikatorlar

DOI: 10.3390/signals6040050

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