research-article

Making muscle-computer interfaces more practical

Authors:

James A. LandayAuthors Info & Claims

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 851 - 854

https://doi.org/10.1145/1753326.1753451

Published: 10 April 2010 Publication History

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Abstract

Recent work in muscle sensing has demonstrated the poten-tial of human-computer interfaces based on finger gestures sensed from electrodes on the upper forearm. While this approach holds much potential, previous work has given little attention to sensing finger gestures in the context of three important real-world requirements: sensing hardware suitable for mobile and off-desktop environments, elec-trodes that can be put on quickly without adhesives or gel, and gesture recognition techniques that require no new training or calibration after re-donning a muscle-sensing armband. In this note, we describe our approach to over-coming these challenges, and we demonstrate average clas-sification accuracies as high as 86% for pinching with one of three fingers in a two-session, eight-person experiment.

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Cited By

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Panigrahy SLee CNagoria VJanghorban MPandey RNittala A(2024)ecSkin: Low-Cost Fabrication of Epidermal Electrochemical Sensors for Detecting Biomarkers in SweatProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642232(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642232
Sehrt JFerreira LWeyers KMahmood AKosch TSchwind V(2024)Improving Electromyographic Muscle Response Times through Visual and Tactile Prior Stimulation in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642091(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642091
Islam MMassicotte DMassicotte PZhu W(2024)Surface EMG-Based Intersession/Intersubject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer LearningIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.338128873(1-16)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3381288
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Index Terms

Making muscle-computer interfaces more practical
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens

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Information & Contributors

Information

Published In

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2010

2690 pages

ISBN:9781605589299

DOI:10.1145/1753326

General Chair:
Elizabeth Mynatt
Georgia Institute of Technology
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Scott Hudson
Carnegie Mellon University
,
Keith Edwards
Georgia Tech
,
Tom Rodden
University of Nottingham

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2010

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CHI '10

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SIGCHI

CHI '10: CHI Conference on Human Factors in Computing Systems

April 10 - 15, 2010

Georgia, Atlanta, USA

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Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

View all

Panigrahy SLee CNagoria VJanghorban MPandey RNittala A(2024)ecSkin: Low-Cost Fabrication of Epidermal Electrochemical Sensors for Detecting Biomarkers in SweatProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642232(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642232
Sehrt JFerreira LWeyers KMahmood AKosch TSchwind V(2024)Improving Electromyographic Muscle Response Times through Visual and Tactile Prior Stimulation in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642091(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642091
Islam MMassicotte DMassicotte PZhu W(2024)Surface EMG-Based Intersession/Intersubject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer LearningIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.338128873(1-16)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3381288
Viekash VDeenadayalan E(2024)Muscle intent-based continuous passive motion machine in a gaming context using a lightweight CNNInternational Journal of Intelligent Robotics and Applications10.1007/s41315-024-00369-4Online publication date: 12-Aug-2024
https://doi.org/10.1007/s41315-024-00369-4
Okamoto MMurao K(2023)PPG2EMG: Estimating Upper-Arm Muscle Activities and EMG from Wrist PPG ValuesSensors10.3390/s2304178223:4(1782)Online publication date: 5-Feb-2023
https://doi.org/10.3390/s23041782
Cárdenas-Bolaño NPolo ARobles-Algarín C(2023)Implementation of an Intelligent EMG Signal Classifier Using Open-Source HardwareComputers10.3390/computers1212026312:12(263)Online publication date: 18-Dec-2023
https://doi.org/10.3390/computers12120263
Tanaka AVisi FDonato BKlang MZbyszyński M(2023)An End-to-End Musical Instrument System That Translates Electromyogram Biosignals to Synthesized SoundComputer Music Journal10.1162/comj_a_0067247:1(64-84)Online publication date: 13-Jun-2023
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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
Sehrt JWißmann TBreitenbach JSchwind V(2023)The Effects of Body Location and Biosignal Feedback Modality on Performance and Workload Using Electromyography in Virtual RealityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580738(1-16)Online publication date: 19-Apr-2023
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Godoy RGuan BDwivedi AShahmohammadi MOwen MLiarokapis M(2023)Multi-Grasp Classification for the Control of Robot Hands Employing Transformers and Lightmyography Signals2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10340274(1-6)Online publication date: 24-Jul-2023
https://doi.org/10.1109/EMBC40787.2023.10340274
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Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces

Advancing Muscle-Computer Interfaces with High-Density Electromyography

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