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CAPG-MYO: A Muscle-Computer Interface Supporting User-defined Gesture Recognition

Authors:

Xiubo LiangAuthors Info & Claims

ICCCM '21: Proceedings of the 9th International Conference on Computer and Communications Management

Pages 52 - 58

https://doi.org/10.1145/3479162.3479170

Published: 28 October 2021 Publication History

Abstract

The recent progress in sEMG-based hand gesture detection has developed a set of predefined hand gestures for interaction. However, customized hand gestures are less concerned due to the lack of supporting tools for training alternative hand gestures. To fill the gap, we present a training system, called CAPG-MYO, for user-defined hand gesture interaction. An armband named CAPG was used to simultaneously capture sEMG and IMU signals from the participants to construct a small-scale dataset with the customized hand gestures. To predict the customized hand gestures, we developed a multiview convolutional neural network to handle the dataset and consequently shaped a gesture recognition model that effectively transferred a model of 8-hand gesture recognition into a model of the customized hand gesture recognition. We conducted technical validation of the system and the results demonstrate the system's accuracy of hand gesture recognition, which reached 84.71% after a 2min training.

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Cited By

Baracat FZanghier MBenin LFarina DIndiveri GBenatti SDonati E(2024)Leveraging Motor Unit Spatial Activation Patterns for Channel Selection in Finger Force Regression2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10781677(1-4)Online publication date: 15-Jul-2024
https://doi.org/10.1109/EMBC53108.2024.10781677
Emimal MHans WInbamalar TLindsay N(2024)Classification of EMG signals with CNN features and voting ensemble classifierComputer Methods in Biomechanics and Biomedical Engineering10.1080/10255842.2024.2310726(1-15)Online publication date: 5-Feb-2024
https://doi.org/10.1080/10255842.2024.2310726
Dai QWong YKankanhali MLi XGeng W(2023)Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture RecognitionBioengineering10.3390/bioengineering1009110110:9(1101)Online publication date: 20-Sep-2023
https://doi.org/10.3390/bioengineering10091101
Show More Cited By

Index Terms

CAPG-MYO: A Muscle-Computer Interface Supporting User-defined Gesture Recognition
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices

Index terms have been assigned to the content through auto-classification.

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Highlights
- We decompose sEMG image into multiple streams by the electrodes layout to model correlation between muscles and gestures.
Graphical abstract

Display Omitted

Abstract
In muscle-computer interface (MCI), deep learning is a promising technology to build-up classifiers for recognizing gestures from surface electromyography (sEMG) signals. Motivated by the observation that a small group of muscles play ...

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cover image ACM Other conferences

ICCCM '21: Proceedings of the 9th International Conference on Computer and Communications Management

July 2021

223 pages

ISBN:9781450390071

DOI:10.1145/3479162

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 October 2021

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National Key Research and Development Program of China

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ICCCM '21

ICCCM '21: The 2021 9th International Conference on Computer and Communications Management

July 16 - 18, 2021

Singapore, Singapore

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Cited By

Baracat FZanghier MBenin LFarina DIndiveri GBenatti SDonati E(2024)Leveraging Motor Unit Spatial Activation Patterns for Channel Selection in Finger Force Regression2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10781677(1-4)Online publication date: 15-Jul-2024
https://doi.org/10.1109/EMBC53108.2024.10781677
Emimal MHans WInbamalar TLindsay N(2024)Classification of EMG signals with CNN features and voting ensemble classifierComputer Methods in Biomechanics and Biomedical Engineering10.1080/10255842.2024.2310726(1-15)Online publication date: 5-Feb-2024
https://doi.org/10.1080/10255842.2024.2310726
Dai QWong YKankanhali MLi XGeng W(2023)Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture RecognitionBioengineering10.3390/bioengineering1009110110:9(1101)Online publication date: 20-Sep-2023
https://doi.org/10.3390/bioengineering10091101
Eddy EScheme EBateman S(2023)A Framework and Call to Action for the Future Development of EMG-Based Input in HCIProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580962(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580962
De Paolis LDe Luca V(2022)The effects of touchless interaction on usability and sense of presence in a virtual environmentVirtual Reality10.1007/s10055-022-00647-126:4(1551-1571)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10055-022-00647-1

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