More Web Proxy on the site http://driver.im/

research-article

Opportunistic synergy: a classifier fusion engine for micro-gesture recognition

Authors:

Leonardo Angelini,

Francesco Carrino,

Stefano Carrino,

Omar Abou Khaled,

Elena MugelliniAuthors Info & Claims

AutomotiveUI '13: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Pages 30 - 37

https://doi.org/10.1145/2516540.2516563

Published: 28 October 2013 Publication History

Abstract

In this paper, we present a novel opportunistic paradigm for in-vehicle gesture recognition. This paradigm allows using two or more subsystems in a synergistic manner: they can work in parallel but the lack of some of them does not compromise the functioning of the whole system. In order to segment and recognize micro-gestures performed by the user on the steering wheel, we combine a wearable approach based on the electromyography of the user's forearm muscles, with an environmental approach based on pressure sensors integrated directly on the steering wheel. We present and analyze several fusion methods and gesture segmentation strategies. A prototype has been developed and evaluated with data from nine subjects. The results prove that the proposed opportunistic system performs equal or better than each stand-alone subsystem while increasing the interaction possibilities.

References

[1]

Riener, A. Driver-vehicle confluence or how to control your car in future? In Proc. of AutomotiveUI '12, no. c, p. 217, 2012.

Digital Library

[2]

Riener, A. Gestural Interaction in Vehicular Applications. Computer, vol. 45, no. 4, pp. 42--47, Apr. 2012.

Digital Library

[3]

Rhodes, B. J., Minar, N., and J. Weaver, J. Wearable computing meets ubiquitous computing: reaping the best of both worlds. In Digest of Papers International Symposium on Wearable Computers, pp. 141--149.

Digital Library

[4]

Wolf, K., Naumann, A., and Rohs, M. A Taxonomy of Microinteractions: Defining Micro-gestures based on Ergonomic and Scenario-dependent Requirements. In Proc. of the 13th IFIP INTERACT, 559--575, 2011.

Digital Library

[5]

Pfleging, B., Schmidt, A., Döring, T., and Knobel, M. AutoNUI. In Proc.of AutomotiveUI '11, 2011, p. 207.

[6]

González, I. E., Wobbrock, J. O., Chau, D. H., Faulring, A., and Myers, B. A. Eyes on the road, hands on the wheel: thumb-based interaction techniques for input on steering wheels. In Proc. of Graphics Interface 2007, 95--102, 2007.

Digital Library

[7]

Döring, T., Kern, D., Marshall, P., Pfeiffer, M., Schöning, J., Gruhn, V., Schmidt, A. Gestural interaction on the steering wheel: reducing the visual demand. In Proc. of CHI '11, pp. 483--492, 2011.

Digital Library

[8]

Mahr, A., Endres, C., Müller, C., and Schneeberger, T. Determining human-centered parameters of ergonomic micro-gesture interaction for drivers using the theater approach. In Proc. of AutomotiveUI '11, 2011, p. 151.

Digital Library

[9]

Angelini, L., Caon, M., Carrino, F., Carrino, S., Lalanne, D., Abou Khaled, O., and Mugellini, E. WheelSense: Enabling Tangible Gestures on the Steering Wheel for In-Car Natural Interaction. In Proc. of HCII 2013 LNCS Vol. 8005, 2013, pp 531--540.

[10]

Khushaba, R. N., Kodagoda, S., Liu, D., and Dissanayake, G. Muscle computer interfaces for driver distraction reduction. Computer methods and programs in biomedicine, vol. 110, no. 2, pp. 137--49, May 2013.

Digital Library

[11]

Carrino, F., Carrino, S., Caon, M., Angelini, L., Abou Khaled, O., and Mugellini, E. In-Vehicle Natural Interaction Based on Electromyography. In Proc. of AutomotiveUI '12, pp. 1--3, 2012.

[12]

Bose, R., Brakensiek, J., and Park, K. Terminal mode. In Proc. of AutomotiveUI '10, 2010, p. 148.

Digital Library

[13]

Carrino, S., Mugellini, E., Abou Khaled, O., Ingold, R. ARAMIS: Toward a Hybrid Approach for Human-Environment Interaction. In Proc. of HCII 2011, Vol. 6763, 2011, pp 165--174.

Digital Library

[14]

Wachs, J. P., Kölsch, M., Stern, H., and Edan, Y. Vision-based hand-gesture applications. Communications of the ACM, vol. 54, no. 2, p. 60, Feb. 2011.

Digital Library

[15]

Costanza, E., Inverso, S., and Allen, R. Toward subtle intimate interfaces for mobile devices using an EMG controller In Proc. of CHI '05, p. 481, 2005.

Digital Library

[16]

"Teaching manual for the formation and examination of driving instructors", "Manuel d'enseignement pour la formation et l'examen des moniteurs de conduite", Département fédéral de justice et police: http://www.asa.ch/media/archive1/Shop/Ausbildungsunterla-gen/gratis/Leitfaden_Fahrlehrer_Fachgruppe_7_f.pdf, (June 2013).

[17]

Sokolova, M., and Lapalme, G. A systematic analysis of performance measures for classification tasks. Information Processing & Management, vol. 45, no. 4, pp. 427--437, Jul. 2009.

Digital Library

[18]

Kittler, J., and Alkoot, F. M. Sum versus vote fusion in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 110--115, Jan. 2003.

Digital Library

[19]

Kittler, J., Hatef, M., Duin, R. P. W., and Matas, J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226--239, Mar. 1998.

Digital Library

[20]

Kuncheva, L. I., Bezdek, J. C., and Duin, R. P. W. Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition, vol. 34, no. 2, pp. 299--314, Feb. 2001.

[21]

Matthews, B. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 1975.

[22]

Jurman, G., Riccadonna, S., and Furlanello, C. A comparison of MCC and CEN error measures in multi-class prediction. PloS one, vol. 7, no. 8, p. e41882, Jan. 2012.

[23]

Caputo, B., Sim, K., Furesjo, F., and Smola, A. Appearance-based object recognition using SVMs: which kernel should I use? Proc. of Neural Information Processing Systems Workshop on Statistical methods for Computational Experiments In Visual Processing and Computer Vision. Whistler, 2001.

[24]

Carrino, S., Péclat, A., Mugellini, E., Abou Khaled, O. and Ingold, R. Humans and smart environments: a novel multimodal interaction approach. Proc. ICMI '11, 2011, p. 105.

Digital Library

[25]

Souza, C. http://sourceforge.net/projects/accord-net/, 2013. (June 2013).

[26]

Tekscan FlexiForce: http://www.tekscan.com/flexible-force-sensors, (June 2013).

[27]

Cram, J. 2011. Cram's introduction to surface electromyography. Jones & Bartlett Publishers.

Cited By

Bilius LAndrei AVatavu R(2024)From Smart Buildings to Smart Vehicles: Mobile User Interfaces for Multi-Environment Interactions2024 International Conference on Development and Application Systems (DAS)10.1109/DAS61944.2024.10541208(152-155)Online publication date: 23-May-2024
https://doi.org/10.1109/DAS61944.2024.10541208
Jansen PColley MRukzio E(2022)A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature ReviewProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35346176:2(1-51)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534617
Bilius LVatavu RMarquardt N(2021)Smart Vehicle Proxemics: A Conceptual Framework Operationalizing Proxemics in the Context of Outside-the-Vehicle InteractionsHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85616-8_11(150-171)Online publication date: 26-Aug-2021
https://doi.org/10.1007/978-3-030-85616-8_11
Show More Cited By

Index Terms

Opportunistic synergy: a classifier fusion engine for micro-gesture recognition
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Alteration of muscle synergy structure while walking under increased postural constraints

It is hypothesised that specific groups of muscles aka muscle synergies (MSs) are combined by the central nervous system to control a wide repertoire of movements and also simplify motor control. Therefore, studying MSs during human locomotion is of ...
Muscle Synergy Analysis on Upper Limb Movements of Human Arms
ICBBE '19: Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering

Upper limb movements of human arms are natural behaviors which require both the spatial and temporal coordination of multiple muscles. We now investigate how the neural strategy control upper limb movements in the human movement control. These upper ...
Optimal Placement of Multi-Channels sEMG Electrod for Finger Movement Classification
ICBBE '17: Proceedings of the 2017 4th International Conference on Biomedical and Bioinformatics Engineering

This research aims to propose the optimal electrode positions for surface EMG by using a modern gesture control device called MYO armband. Seven healthy volunteers participated in this research. The sEMG signal was collected form three different ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

AutomotiveUI '13: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

October 2013

281 pages

ISBN:9781450324786

DOI:10.1145/2516540

Conference Chair:
Jacques Terken
Eindhoven University of Technology, Eindhoven, The Netherlands
,
Program Chairs:
Marieke Martens
TNO / Universiteit Twente, Twente, The Netherlands
,
Christian Müller
DFKI, Saarbrücken, Germany
,
Jennifer Healey
INTEL Labs, Santa Clara, USA
,
Publications Chair:
Sebastian Osswald
Technische Universität München, TUM CREATE, Singapore

Copyright © 2013 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]

Sponsors

Eindhoven University of Technology Department of Industrial Design: Eindhoven University of Technology, Department of Industrial Design

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AutomotiveUI '13

Sponsor:

Eindhoven University of Technology Department of Industrial Design

AutomotiveUI '13: Automotive User Interfaces and Interactive Vehicular Applications

October 28 - 30, 2013

Eindhoven, Netherlands

Acceptance Rates

AutomotiveUI '13 Paper Acceptance Rate 41 of 67 submissions, 61%;

Overall Acceptance Rate 248 of 566 submissions, 44%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
351
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bilius LAndrei AVatavu R(2024)From Smart Buildings to Smart Vehicles: Mobile User Interfaces for Multi-Environment Interactions2024 International Conference on Development and Application Systems (DAS)10.1109/DAS61944.2024.10541208(152-155)Online publication date: 23-May-2024
https://doi.org/10.1109/DAS61944.2024.10541208
Jansen PColley MRukzio E(2022)A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature ReviewProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35346176:2(1-51)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534617
Bilius LVatavu RMarquardt N(2021)Smart Vehicle Proxemics: A Conceptual Framework Operationalizing Proxemics in the Context of Outside-the-Vehicle InteractionsHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85616-8_11(150-171)Online publication date: 26-Aug-2021
https://doi.org/10.1007/978-3-030-85616-8_11
Bilius LVatavu R(2020)A Synopsis of Input Modalities for In-Vehicle Infotainment and Consumption of Interactive MediaProceedings of the 2020 ACM International Conference on Interactive Media Experiences10.1145/3391614.3399400(195-199)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1145/3391614.3399400
Lee SYoon S(2020)User interface for in-vehicle systems with on-wheel finger spreading gestures and head-up displaysJournal of Computational Design and Engineering10.1093/jcde/qwaa052Online publication date: 19-Jun-2020
https://doi.org/10.1093/jcde/qwaa052
Bilius LVatavu R(2020)A multistudy investigation of drivers and passengers’ gesture and voice input preferences for in-vehicle interactionsJournal of Intelligent Transportation Systems10.1080/15472450.2020.1846127(1-24)Online publication date: 18-Nov-2020
https://doi.org/10.1080/15472450.2020.1846127
Qin RLiu YSwash MLi MMeng HLei TChen T(2019)A Fast Automatic Holoscopic 3D Micro-gesture Recognition System for Immersive ApplicationsAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery10.1007/978-3-030-32591-6_74(696-703)Online publication date: 7-Nov-2019
https://doi.org/10.1007/978-3-030-32591-6_74
Chen BHuang STsai W(2017)Eliminating Driving Distractions: Human-Computer Interaction with Built-In ApplicationsIEEE Vehicular Technology Magazine10.1109/MVT.2016.262533112:1(20-29)Online publication date: Mar-2017
https://doi.org/10.1109/MVT.2016.2625331
Angelini LBaumgartner JCarrino FCarrino SCaon MKhaled OSauer JLalanne DMugellini ESonderegger ALalanne DNigay L(2016)A comparison of three interaction modalities in the carActes de la 28ième conference francophone sur l'Interaction Homme-Machine10.1145/3004107.3004118(188-196)Online publication date: 25-Oct-2016
https://dl.acm.org/doi/10.1145/3004107.3004118
Angelini LLalanne DHoven EKhaled OMugellini E(2015)Move, Hold and Touch: A Framework for Tangible Gesture Interactive SystemsMachines10.3390/machines30301733:3(173-207)Online publication date: 18-Aug-2015
https://doi.org/10.3390/machines3030173
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents