Gesture Prediction Using Surface-EMG Signals

Sibani Panigrahi; Sohham Seal; Shyam Lal; Ganesh Naik

doi:10.1007/978-3-031-90577-3_37

Gesture Prediction Using Surface-EMG Signals

Sibani Panigrahi, Sohham Seal, Shyam Lal, Ganesh Naik

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

Gesture prediction plays a crucial role in enhancing human-computer interaction by enabling intuitive and natural control methods, thereby reducing reliance on traditional input devices. It significantly improves accessibility for individuals with physical disabilities by providing alternative means of communication and control. Moreover, gesture prediction has broad applications in fields such as robotics, virtual reality, and prosthetics, enhancing both the functionality and user experience of these technologies. This study presents the design and development of an Electromyogram (EMG) signal-based gesture recognition system utilizing recent Deep Learning (DL) techniques. The Hyser EMG dataset was used for experimentation, and its data was pre-processed and analyzed using both sliding window and a combination of sliding window and Fourier transform methods. The performance of the EMG signal-based gesture recognition system was evaluated and compared across different DL models. The results demonstrate that RCCGNet-based gesture prediction outperforms other models.

Original language	English
Title of host publication	Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing - 3rd International Conference, PCEMS 2024, Revised Selected Papers
Editors	Deep Gupta, Vipin Kamble, Vishal Satpute, Ashwin Kothari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	438-449
Number of pages	12
ISBN (Print)	9783031905766
DOIs	https://doi.org/10.1007/978-3-031-90577-3_37
Publication status	Published - 2025
Externally published	Yes
Event	3rd International Conference on Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing, PCEMS 2024 - Nagpur, India Duration: 11 Nov 2024 → 12 Nov 2024

Publication series

Name	Communications in Computer and Information Science
Volume	2491 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing, PCEMS 2024
Country/Territory	India
City	Nagpur
Period	11/11/24 → 12/11/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

AlexNet
Fourier Transform
Gesture Identification
RCCGNet
ResNet
Sliding Window Method
VGGNet

Access to Document

10.1007/978-3-031-90577-3_37

Cite this

Panigrahi, S., Seal, S., Lal, S., & Naik, G. (2025). Gesture Prediction Using Surface-EMG Signals. In D. Gupta, V. Kamble, V. Satpute, & A. Kothari (Eds.), Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing - 3rd International Conference, PCEMS 2024, Revised Selected Papers (pp. 438-449). (Communications in Computer and Information Science; Vol. 2491 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-90577-3_37

Panigrahi, Sibani ; Seal, Sohham ; Lal, Shyam et al. / Gesture Prediction Using Surface-EMG Signals. Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing - 3rd International Conference, PCEMS 2024, Revised Selected Papers. editor / Deep Gupta ; Vipin Kamble ; Vishal Satpute ; Ashwin Kothari. Springer Science and Business Media Deutschland GmbH, 2025. pp. 438-449 (Communications in Computer and Information Science).

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abstract = "Gesture prediction plays a crucial role in enhancing human-computer interaction by enabling intuitive and natural control methods, thereby reducing reliance on traditional input devices. It significantly improves accessibility for individuals with physical disabilities by providing alternative means of communication and control. Moreover, gesture prediction has broad applications in fields such as robotics, virtual reality, and prosthetics, enhancing both the functionality and user experience of these technologies. This study presents the design and development of an Electromyogram (EMG) signal-based gesture recognition system utilizing recent Deep Learning (DL) techniques. The Hyser EMG dataset was used for experimentation, and its data was pre-processed and analyzed using both sliding window and a combination of sliding window and Fourier transform methods. The performance of the EMG signal-based gesture recognition system was evaluated and compared across different DL models. The results demonstrate that RCCGNet-based gesture prediction outperforms other models.",

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Panigrahi, S, Seal, S, Lal, S & Naik, G 2025, Gesture Prediction Using Surface-EMG Signals. in D Gupta, V Kamble, V Satpute & A Kothari (eds), Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing - 3rd International Conference, PCEMS 2024, Revised Selected Papers. Communications in Computer and Information Science, vol. 2491 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 438-449, 3rd International Conference on Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing, PCEMS 2024, Nagpur, India, 11/11/24. https://doi.org/10.1007/978-3-031-90577-3_37

Gesture Prediction Using Surface-EMG Signals. / Panigrahi, Sibani; Seal, Sohham; Lal, Shyam et al.
Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing - 3rd International Conference, PCEMS 2024, Revised Selected Papers. ed. / Deep Gupta; Vipin Kamble; Vishal Satpute; Ashwin Kothari. Springer Science and Business Media Deutschland GmbH, 2025. p. 438-449 (Communications in Computer and Information Science; Vol. 2491 CCIS).

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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AU - Panigrahi, Sibani

AU - Seal, Sohham

AU - Lal, Shyam

AU - Naik, Ganesh

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Gesture prediction plays a crucial role in enhancing human-computer interaction by enabling intuitive and natural control methods, thereby reducing reliance on traditional input devices. It significantly improves accessibility for individuals with physical disabilities by providing alternative means of communication and control. Moreover, gesture prediction has broad applications in fields such as robotics, virtual reality, and prosthetics, enhancing both the functionality and user experience of these technologies. This study presents the design and development of an Electromyogram (EMG) signal-based gesture recognition system utilizing recent Deep Learning (DL) techniques. The Hyser EMG dataset was used for experimentation, and its data was pre-processed and analyzed using both sliding window and a combination of sliding window and Fourier transform methods. The performance of the EMG signal-based gesture recognition system was evaluated and compared across different DL models. The results demonstrate that RCCGNet-based gesture prediction outperforms other models.

AB - Gesture prediction plays a crucial role in enhancing human-computer interaction by enabling intuitive and natural control methods, thereby reducing reliance on traditional input devices. It significantly improves accessibility for individuals with physical disabilities by providing alternative means of communication and control. Moreover, gesture prediction has broad applications in fields such as robotics, virtual reality, and prosthetics, enhancing both the functionality and user experience of these technologies. This study presents the design and development of an Electromyogram (EMG) signal-based gesture recognition system utilizing recent Deep Learning (DL) techniques. The Hyser EMG dataset was used for experimentation, and its data was pre-processed and analyzed using both sliding window and a combination of sliding window and Fourier transform methods. The performance of the EMG signal-based gesture recognition system was evaluated and compared across different DL models. The results demonstrate that RCCGNet-based gesture prediction outperforms other models.

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A2 - Satpute, Vishal

A2 - Kothari, Ashwin

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 11 November 2024 through 12 November 2024

ER -

Panigrahi S, Seal S, Lal S, Naik G. Gesture Prediction Using Surface-EMG Signals. In Gupta D, Kamble V, Satpute V, Kothari A, editors, Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing - 3rd International Conference, PCEMS 2024, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2025. p. 438-449. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-90577-3_37

Gesture Prediction Using Surface-EMG Signals

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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