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2024 | OriginalPaper | Chapter

Finger Movement Classification from EMG Signals Using Gaussian Mixture Model

Authors : Mehmet Emin Aktan, Merve Aktan Süzgün, Erhan Akdoğan, Tuğçe Özekli Mısırlıoğlu

Published in: Advances in Intelligent Manufacturing and Service System Informatics

Publisher: Springer Nature Singapore

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Abstract

The chapter delves into the critical role of EMG signals in evaluating muscle activity for hand rehabilitation and prosthesis applications. It introduces the use of the Gaussian Mixture Model for classifying finger movements, with a focus on the pre-processing steps and the Log-Linearized Gaussian Mixture Network (LLGMN) model. The study involves real-time data collection from three subjects, highlighting the challenges and potential improvements in movement classification, particularly for the index finger. The results demonstrate the feasibility of using EMG signals for precise movement detection, with implications for enhancing the effectiveness of therapeutic and prosthetic devices.

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Title: Finger Movement Classification from EMG Signals Using Gaussian Mixture Model
Authors: Mehmet Emin Aktan
Merve Aktan Süzgün
Erhan Akdoğan
Tuğçe Özekli Mısırlıoğlu
Publisher: Springer Nature Singapore
Book: Advances in Intelligent Manufacturing and Service System Informatics
Print ISBN: 978-981-9960-61-3

Electronic ISBN: 978-981-9960-62-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-6062-0_22

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