Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

Putro, Nur Achmad Sulistyo; Avian, Cries; Prakosa, Setya Widyawan; Mahali, Muhammad Izzuddin; Leu, Jenq-Shiou

doi:10.1016/j.bspc.2023.105447

Cited by 11 publications

(2 citation statements)

References 44 publications

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“…complex convolutional structures, a result demonstrated by the performance of RPC-Net if compared to our implementation of the solutions proposed by Quivira et al [46], Olsson et al [45], and Putro et al [47]. A simple architecture implies a reduced computational cost and makes RPC-Net potentially embeddable in a microprocessor of a prosthetic device.…”

Section: Discussionmentioning

confidence: 58%

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Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation

Rolandino,

Gagliardi,

Martins

et al. 2024

IEEE Trans. Biomed. Eng.

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The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position with high accuracy and computational efficiency. Methods: RPC-Net uses a regression-based approach to convert forearm electromyographic signals into hand kinematics. We tested the adaptability of the algorithm to different conditions and compared its performance with that of solutions from the academic literature. Results: RPC-Net demonstrated a high degree of accuracy in predicting hand position from electromyographic activity, outperforming other solutions with the same computational cost. Including previous position data consistently improved results across subjects and conditions. RPC-Net showed robustness against a reduction in the number of electromyography electrodes used and shorter input signals, indicating potential for further reduction in computational cost. Conclusion: The results demonstrate that RPC-Net is capable of accurately translating forearm electromyographic activity into hand position, offering a practical and adaptable tool that may be accessible in clinical settings. Significance: The development of RPC-Net represents a significant advancement. In clinical settings, its application could enable prosthetic devices to be controlled in a way that feels more natural, improving the quality of life for individuals with limb loss.

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Section: Discussionmentioning

confidence: 58%

“…2) The recurrent neural network proposed in [46], originally with hidden size of 50 and 10 outputs, modified to have hidden size of 224 and 24 outputs (RNN-Qui). 3) The transformer architecture introduced in [47]. The original implementation has 1024 as dimension of model, 4 transformer layers and 22 outputs.…”

Section: G Comparison With Other Solutionsmentioning

confidence: 99%

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation

Rolandino,

Gagliardi,

Martins

et al. 2024

IEEE Trans. Biomed. Eng.

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Evaluation of long-short-term memory on simultaneous and proportional myoelectric control system for individual finger movements

Anam,

Rizal,

Ilyas

et al. 2024

Res. Biomed. Eng.

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Artificial intelligence on biomedical signals: technologies, applications, and future directions

Lee,

Park,

Kim

et al. 2024

Med-X

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Integrating artificial intelligence (AI) into biomedical signal analysis represents a significant breakthrough in enhanced precision and efficiency of disease diagnostics and therapeutics. From traditional computational models to advanced machine learning algorithms, AI technologies have improved signal processing by efficiently handling complexity and interpreting intricate datasets. Understanding physiological data, which requires highly trained professionals, is now more accessible; in regions with limited access, AI tools expand healthcare accessibility by providing high-level diagnostic insights, ultimately improving health outcomes. This review explores various AI methodologies, including supervised, unsupervised, and reinforcement learning, and examines their synergy for biomedical signal analysis and future directions in medical science. By capturing a comprehensive overview of the current state and prospects of AI-driven healthcare, this paper highlights the transformative potential of AI in analyzing biomedical signals. Graphical Abstract

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Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

Cited by 11 publications

References 44 publications

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation

Evaluation of long-short-term memory on simultaneous and proportional myoelectric control system for individual finger movements

Artificial intelligence on biomedical signals: technologies, applications, and future directions

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