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Volume 6, September
 
 

Signals, Volume 6, Issue 4 (December 2025) – 1 article

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17 pages, 2255 KB  
Article
Electromyography-Based Sign Language Recognition: A Low-Channel Approach for Classifying Fruit Name Gestures
by Kudratjon Zohirov, Mirjakhon Temirov, Sardor Boykobilov, Golib Berdiev, Feruz Ruziboev, Khojiakbar Egamberdiev, Mamadiyor Sattorov, Gulmira Pardayeva and Kuvonch Madatov
Signals 2025, 6(4), 50; https://doi.org/10.3390/signals6040050 - 25 Sep 2025
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Signal Detecting and Processing)
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