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From One to Many Users and Contexts: A Classifier for Hand and Arm Gestures

Authors:

Carlos DuarteAuthors Info & Claims

IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces

Pages 115 - 120

https://doi.org/10.1145/2678025.2701388

Published: 18 March 2015 Publication History

Abstract

On-body interaction techniques are gaining traction, and opening up new avenues to control interactive systems. At the same time, they reveal potential to increase the accessibility of systems like touch based smartphones and other mobile devices for visually impaired users. However, for this potential to be realised, it is paramount that these techniques can be used in a multitude of contextual settings, and, ideally, do not impose training and calibration procedures. Our approach intends to optimize signal filtering, feature extraction parameters and classifier configurations for each defined gesture. The results show a 98.35% accuracy for the optimized classifier. We proceeded to conduct a validation study (15 participants) in three contexts: seated, standing and walking. Our findings show that, despite the gesture being trained by someone not participating in the study, the average accuracy was 94.67%. We also concluded that, while walking, false positives can impact its usefulness.

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Cited By

Wang KChen YZhang YYang XHu C(2022)Multi-Source Integration based Transfer Learning Method for Cross-User sEMG Gesture Recognition2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892711(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892711
Shatilov KChatzopoulos DLee LHui P(2021)Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up SurveyACM Transactions on Interactive Intelligent Systems10.1145/345795011:2(1-49)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3457950
Costa DDuarte C(2019)Factors that Impact the Acceptability of On-Body Interaction by Users with Visual ImpairmentsHuman-Computer Interaction – INTERACT 201910.1007/978-3-030-29381-9_17(267-287)Online publication date: 2-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-29381-9_17
Show More Cited By

Index Terms

From One to Many Users and Contexts: A Classifier for Hand and Arm Gestures
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens

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Published In

cover image ACM Conferences

IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces

March 2015

480 pages

ISBN:9781450333061

DOI:10.1145/2678025

General Chairs:
Oliver Brdiczka
Vectra Networks, Inc.
,
Polo Chau
Georgia Tech
,
Program Chairs:
Giuseppe Carenini
University of British Columbia
,
Shimei Pan
University of Maryland
,
Per Ola Kristensson
University of Cambridge

Copyright © 2015 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: 18 March 2015

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IUI'15

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IUI'15: IUI'15 20th International Conference on Intelligent User Interfaces

March 29 - April 1, 2015

Georgia, Atlanta, USA

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IUI '15 Paper Acceptance Rate 47 of 205 submissions, 23%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

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Cited By

Wang KChen YZhang YYang XHu C(2022)Multi-Source Integration based Transfer Learning Method for Cross-User sEMG Gesture Recognition2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892711(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892711
Shatilov KChatzopoulos DLee LHui P(2021)Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up SurveyACM Transactions on Interactive Intelligent Systems10.1145/345795011:2(1-49)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3457950
Costa DDuarte C(2019)Factors that Impact the Acceptability of On-Body Interaction by Users with Visual ImpairmentsHuman-Computer Interaction – INTERACT 201910.1007/978-3-030-29381-9_17(267-287)Online publication date: 2-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-29381-9_17
Costa DBrdiczka OChau PCarenini GPan SKristensson P(2015)On-Body Interaction for Optimized AccessibilityCompanion Proceedings of the 20th International Conference on Intelligent User Interfaces10.1145/2732158.2732164(121-124)Online publication date: 29-Mar-2015
https://dl.acm.org/doi/10.1145/2732158.2732164

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