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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI for Human Learning and Behavior Change
Volume 8 - 2025 | doi: 10.3389/frai.2025.1506042
This article is part of the Research Topic Human-Centered Artificial Intelligence in Interaction Processes View all 8 articles

Gaussian Process Latent Variable Models-ANN Based Method for Automatic Features Selection and Dimensionality Reduction for Control of EMG-Driven Systems

Provisionally accepted
Maham Nayab Maham Nayab 1Asim Waris Asim Waris 1*Muhammad Jawad Khan Muhammad Jawad Khan 2Dokhyl Alqahtani Dokhyl Alqahtani 2Ahmed Imran Ahmed Imran 3Syed Omer Gilani Syed Omer Gilani 4Umer Hameed Umer Hameed 3
  • 1 National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • 2 Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
  • 3 Ajman University, Ajman, Ajman, United Arab Emirates
  • 4 Abu Dhabi University, Abu Dhabi, United Arab Emirates

The final, formatted version of the article will be published soon.

    Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. This approach aims to improve the classification accuracy of EMG signals while substantially reducing computational costs, offering valuable implications for all EMG-related processes on such data. The proposed methodology involves extracting time and frequency domain features from twelve channels of EMG signals, followed by dimensionality reduction using techniques such as PCA, LDA, PPCA, Lasso and GPLVM, and classification using an ANN. Our investigation revealed that LDA is not appropriate for this dataset. The dimensionality reduction models did not have any significant effect on the accuracy, but the computational cost decreased significantly. In individual comparisons, GPLVM had the shortest computational time (29 s), which was significantly less than that of all the other models (p<0.05), with PCA following at approximately 35 s and Relief at approximately 57s, while PPCA took approximately 69s, and Lasso exhibited higher computational costs than all the models but lower computational costs than did the original set. Using the best-performing features, all possible sets of 2, 3, 4 and 5 features were tested, and the 5-feature set exhibited the best performance. This research demonstrates the effectiveness of dimensionality reduction and feature selection in improving the accuracy of movement recognition in myoelectric control.

    Keywords: ANN, dimensionality reduction, Feature Selection, GPLVM, myoelectric control, PCA

    Received: 04 Oct 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Nayab, Waris, Jawad Khan, Alqahtani, Imran, Gilani and Hameed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Asim Waris, National University of Sciences and Technology (NUST), Islamabad, Pakistan

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