Preprint

EMG-Based Recognition of Lower Limb Movements in Athletes: A Comparative Study of Classification Techniques

Kudratjon ZohirovSoftware and Hardware Support of Computer Systems , Karshi State Technical University , Karshi , UzbekistanSarvar MakhmudjanovArtificial Intelligence , Tashkent University of Information Technologies , Tashkent , UzbekistanFeruz RuziboevConvergence of Digital Technologies , Tashkent University of Information Technologies , Tashkent , UzbekistanGolib BerdievSoftware and Hardware Support of Computer Systems , Karshi State Technical University , Karshi , UzbekistanMirjakhon TemirovConvergence of Digital Technologies , Tashkent University of Information Technologies , Tashkent , UzbekistanGulrukh SherboboyevaInformation Systems and Technologies , Karshi State Technical University , Karshi , UzbekistanFiruza AchilovaInformation Systems and Technologies , Karshi State Technical University , Karshi , UzbekistanGulmira PardayevaInformation Technology , University of Information Technology and Management , Karshi , UzbekistanSardor BoykobilovSoftware and Hardware Support of Computer Systems , Karshi State Technical University , Karshi , Uzbekistan

2025en

ABI

Annotatsiya

In this article, the classification of signals arising from the movements of the lower limb of the leg (LLL) based on electromyography (EMG) (walking, sitting, up and down the stairs) was carried out. In the data collection process, 25 athletes aged 15-22 were involved, and two types of data sets (DS-dataset) were formed using FreeEMG and Biosignalsplux devices. Six important time and frequency domain features were extracted from the EMG signals — RMS (Root Mean Square), MAV (Mean Absolute Value), WL (Waveform Length), ZC (Zero Crossing), MDF (Median Frequency) and SSC (Slope Sign Changes). Several classification algorithms were used to detect and classify movements, including RF (Random Forest), NN (Neural Network), SVM (Support Vector Machine), k-NN (k-Nearest Neighbors) and LR (Logistic Regression) models. Analysis of the experimental results showed that the RF algorithm achieved the highest accuracy of 98.7% when classified with DS collected via the Biosignalsplux device, demonstrating an advantage in terms of performance in motion recognition.

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Identifikatorlar

DOI: 10.20944/preprints202507.2188.v1

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