• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/

A Study on Machine Learning-Based Real-Time Gesture Classification Using EMG Data


Ha-Je Park, Hee-Young Yang, So-Jin Choi, Dae-Yeon Kim, Choon-Sung Nam, Journal of Internet Computing and Services, Vol. 25, No. 2, pp. 57-67, Apr. 2024
10.7472/jksii.2024.25.2.57, Full Text:
Keywords: EMG, Machine Learning, Real time gestuer classification, EMG data preprocessing

Abstract

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.


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Cite this article
[APA Style]
Park, H., Yang, H., Choi, S., Kim, D., & Nam, C. (2024). A Study on Machine Learning-Based Real-Time Gesture Classification Using EMG Data. Journal of Internet Computing and Services, 25(2), 57-67. DOI: 10.7472/jksii.2024.25.2.57.

[IEEE Style]
H. Park, H. Yang, S. Choi, D. Kim, C. Nam, "A Study on Machine Learning-Based Real-Time Gesture Classification Using EMG Data," Journal of Internet Computing and Services, vol. 25, no. 2, pp. 57-67, 2024. DOI: 10.7472/jksii.2024.25.2.57.

[ACM Style]
Ha-Je Park, Hee-Young Yang, So-Jin Choi, Dae-Yeon Kim, and Choon-Sung Nam. 2024. A Study on Machine Learning-Based Real-Time Gesture Classification Using EMG Data. Journal of Internet Computing and Services, 25, 2, (2024), 57-67. DOI: 10.7472/jksii.2024.25.2.57.