research-article

Information capacity of full-body movements

Authors:

Antti Oulasvirta,

Laura LeppänenAuthors Info & Claims

CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 1289 - 1298

https://doi.org/10.1145/2470654.2466169

Published: 27 April 2013 Publication History

Abstract

We present a novel metric for information capacity of full-body movements. It accommodates HCI scenarios involving continuous movement of multiple limbs. Throughput is calculated as mutual information in repeated motor sequences. It is affected by the complexity of movements and the precision with which an actor reproduces them. Computation requires decorrelating co-dependencies of movement features (e.g., wrist and elbow) and temporal alignment of sequences. HCI researchers can use the metric as an analysis tool when designing and studying user interfaces.

References

[1]

Accot, J., and Zhai, S. Performance evaluation of input devices in trajectory-based tasks: an application of the steering law. Proc. CHI'99, ACM Press (1999), pp. 466--472.

Digital Library

[2]

Berndt, D., and Clifford, J. Using dynamic time warping to find patterns in time series. In AAAI-94 workshop on knowledge discovery in databases, vol. 2 (1994).

[3]

Brillinger, D. R. Some data analyses using mutual information. Brazilian J. Probability and Statistics 18, (2004), 163--183.

[4]

Cover, T., Thomas, J., and Wiley, J. Elements of Information Theory. Wiley Online Library, 1991.

Digital Library

[5]

Enoka, R. Neuromechanics of human movement. Human Kinetics Publishers, 2008.

[6]

Fitts, P. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47, 6 (1954), 381.

[7]

Fitts, P., and Posner, M. Human Performance. Brooks/Cole, Belmont, CA, 1967.

[8]

Kendall, M. G., and Stuart, A. The Advanced Theory of Statistics, Hafner, New York, 1968.

[9]

Lashley, K. An examination of the continuity theory as applied to discriminative learning. Journal of General Psychology (1942).

[10]

Lawrence, N. Probabilistic non-linear principal component analysis with gaussian process latent variable models. The Journal of Machine Learning Research 6 (2005), 1783--1816.

Digital Library

[11]

Liu, L., and van Liere, R. The effect of varying path properties in path steering. Proc. EGVE/EuroVR/VEC, Eurographics Assoc. (2010), pp. 9--16.

Digital Library

[12]

MacKenzie, I. A note on the information-theoretic basis for Fitts' law. Journal of Motor Behavior 21, 3 (1989), 323--330.

[13]

MacKenzie, I. Fitts' law as a research and design tool in human-computer interaction. Human-computer interaction 7, 1 (1992), 91--139.

Digital Library

[14]

Mechsner, F., Kerzel, D., Knoblich, G., and Prinz, W. Perceptual basis of bimanual coordination. Nature 414, 6859 (2001), 69--72.

[15]

Pew, R. Performance of human operators in a three-state relay control system with velocity-augmented displays. IEEE Transactions on Human Factors in Electronics, 2 (1966).

[16]

Rasmussen, C., and Williams, C. Gaussian Processes for Machine Learning. MIT Press, Cambridge, 2006.

Digital Library

[17]

Soukoreff, R., and MacKenzie, I. Towards a standard for pointing device evaluation: Perspectives on 27 years of Fitts' law research in HCI. International Journal of HumanComputer Studies 61, 6 (2004), 751--789.

Digital Library

[18]

Wang, J., Fleet, D., and Hertzmann, A. Gaussian process dynamical models for human motion. IEEE TPAMI 30, 2 (2008), 283--298.

Digital Library

[19]

Williamson, J., and Murray-Smith, R. Rewarding the original: explorations in joint user-sensor motion spaces. Proc. CHI'12, ACM Press (2012), pp. 1717--1726.

Digital Library

[20]

Winter, D., Sidwall, H., and Hobson, D. Measurement and reduction of noise in kinematics of locomotion. Journal of biomechanics 7, 2 (1974), 157--159.

[21]

Zhai, S. Characterizing computer input with Fitts' law parameters: The information and non-information aspects of pointing. IJHCS 61, 6 (2004), 791--809.

Digital Library

[22]

Zhou, F., and De la Torre, F. Canonical time warping for alignment of human behavior. Proc. NIPS, (2009), 1--9.

Cited By

Liu YSra M(2024)DanceGen: Supporting Choreography Ideation and Prototyping with Generative AIProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661594(920-938)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661594
Liu YSra M(2024)Exploring AI-assisted Ideation and Prototyping for ChoreographyCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645227(11-17)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645227
Joung JKim J(2023)Comparison of Audiovisual Components of Dance in Novices and Experts’ Aesthetic Interest PerceptionsEmpirical Studies of the Arts10.1177/0276237423115277541:2(591-622)Online publication date: 7-Mar-2023
https://doi.org/10.1177/02762374231152775
Show More Cited By

Index Terms

Information capacity of full-body movements
1. Human-centered computing

Recommendations

Analysis of Movement Quality in Full-Body Physical Activities

Full-body human movement is characterized by fine-grain expressive qualities that humans are easily capable of exhibiting and recognizing in others’ movement. In sports (e.g., martial arts) and performing arts (e.g., dance), the same sequence of ...
Indirect gaze estimation from body movements based on relationship between gaze and body movements
ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications

This paper proposes indirect gaze estimation from body movements as an alternative to directly observing the eye regions, as done by conventional eye trackers. This is based on the fact that changes in gaze cause corresponding changes in the movements ...
Qualitative Evaluation of Full Body Movements with Gesture Description Language
AIMS '14: Proceedings of the 2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation

The motivation for writing this paper is to present the method for qualitative evaluation of full body movements with Gesture Description Language. By qualitative evaluation we understand the possibility of evaluating the similarity between the template ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2013

3550 pages

ISBN:9781450318990

DOI:10.1145/2470654

General Chair:
Wendy E. Mackay
INRIA
,
Program Chairs:
Stephen Brewster
Glasgow University
,
Susanne Bødker
University of Aarhus

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

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 April 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '13

Sponsor:

SIGCHI

CHI '13: CHI Conference on Human Factors in Computing Systems

April 27 - May 2, 2013

Paris, France

Acceptance Rates

CHI '13 Paper Acceptance Rate 392 of 1,963 submissions, 20%;

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

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
630
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu YSra M(2024)DanceGen: Supporting Choreography Ideation and Prototyping with Generative AIProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661594(920-938)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661594
Liu YSra M(2024)Exploring AI-assisted Ideation and Prototyping for ChoreographyCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645227(11-17)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645227
Joung JKim J(2023)Comparison of Audiovisual Components of Dance in Novices and Experts’ Aesthetic Interest PerceptionsEmpirical Studies of the Arts10.1177/0276237423115277541:2(591-622)Online publication date: 7-Mar-2023
https://doi.org/10.1177/02762374231152775
Zhou QChua CKnibbe JGoncalves JVelloso EKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Dance and Choreography in HCI: A Two-Decade RetrospectiveProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445804(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445804
Wakatsuki TYamada N(2020)Difference Between Intentional and Reactive Movement in Side-Steps: Patterns of Temporal Structure and Force ExertionFrontiers in Psychology10.3389/fpsyg.2020.0218611Online publication date: 2-Sep-2020
https://doi.org/10.3389/fpsyg.2020.02186
Lee JLee S(2019)Automatic path generation for group dance performance using a genetic algorithmMultimedia Tools and Applications10.1007/s11042-018-6493-478:6(7517-7541)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11042-018-6493-4
Berdahl EBlandino MBaker DShanahan DFagerlönn J(2016)An Approach for Using Information Theory to Investigate Continuous Control of Analog Sensors by HumansProceedings of the Audio Mostly 201610.1145/2986416.2986450(85-90)Online publication date: 4-Oct-2016
https://dl.acm.org/doi/10.1145/2986416.2986450
Buschek DDe Luca AAlt FKaye JDruin ALampe CMorris DHourcade J(2016)Evaluating the Influence of Targets and Hand Postures on Touch-based Behavioural BiometricsProceedings of the 2016 CHI Conference on Human Factors in Computing Systems10.1145/2858036.2858165(1349-1361)Online publication date: 7-May-2016
https://dl.acm.org/doi/10.1145/2858036.2858165
Kant V(2016)Extending Cognitive Work Analysis for embodiment: ecological psychology, activity theory and Worker Competency AnalysisTheoretical Issues in Ergonomics Science10.1080/1463922X.2016.124374018:6(548-572)Online publication date: 20-Oct-2016
https://doi.org/10.1080/1463922X.2016.1243740
Ahmed IYe YBhattacharya SAsokan NJacucci GNurmi PTarkoma SMase KLangheinrich MGatica-Perez DGellersen HChoudhury TYatani K(2015)Checksum gesturesProceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2750858.2807521(391-401)Online publication date: 7-Sep-2015
https://dl.acm.org/doi/10.1145/2750858.2807521
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