Control theoretic models of pointing

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Control theoretic models of pointing. / Müller, Jörg; Oulasvirta, Antti; Murray-Smith, Roderick.

In: ACM Transactions on Computer-Human Interaction, Vol. 24, No. 4, 27, 15.09.2017.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Müller, J, Oulasvirta, A & Murray-Smith, R 2017, 'Control theoretic models of pointing', ACM Transactions on Computer-Human Interaction, vol. 24, no. 4, 27. https://doi.org/10.1145/3121431

APA

Müller, J., Oulasvirta, A., & Murray-Smith, R. (2017). Control theoretic models of pointing. ACM Transactions on Computer-Human Interaction, 24(4), [27]. https://doi.org/10.1145/3121431

CBE

Müller J, Oulasvirta A, Murray-Smith R. 2017. Control theoretic models of pointing. ACM Transactions on Computer-Human Interaction. 24(4):Article 27. https://doi.org/10.1145/3121431

MLA

Müller, Jörg, Antti Oulasvirta and Roderick Murray-Smith. "Control theoretic models of pointing". ACM Transactions on Computer-Human Interaction. 2017. 24(4). https://doi.org/10.1145/3121431

Vancouver

Müller J, Oulasvirta A, Murray-Smith R. Control theoretic models of pointing. ACM Transactions on Computer-Human Interaction. 2017 Sep 15;24(4). 27. https://doi.org/10.1145/3121431

Author

Müller, Jörg ; Oulasvirta, Antti ; Murray-Smith, Roderick. / Control theoretic models of pointing. In: ACM Transactions on Computer-Human Interaction. 2017 ; Vol. 24, No. 4.

Bibtex

@article{fca130810db44c36be3fe2d6ea948bc2,
title = "Control theoretic models of pointing",
abstract = "This article presents an empirical comparison of four models from manual control theory on their ability to model targeting behaviour by human users using a mouse: McRuer's Crossover, Costello's Surge, secondorder lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data.We present the use of time-series, phase space, and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts' law based approaches in HCI, with models providing representations and predictions of human pointing dynamics, which can improve our understanding of pointing and inform design.",
keywords = "Aimed movements, Control theory, Dynamics, Fitts' law, Modelling, Pointing, Targeting",
author = "J{\"o}rg M{\"u}ller and Antti Oulasvirta and Roderick Murray-Smith",
year = "2017",
month = sep,
day = "15",
doi = "10.1145/3121431",
language = "English",
volume = "24",
journal = "A C M Transactions on Computer - Human Interaction",
issn = "1073-0516",
publisher = "ACM",
number = "4",

}

RIS

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T1 - Control theoretic models of pointing

AU - Müller, Jörg

AU - Oulasvirta, Antti

AU - Murray-Smith, Roderick

PY - 2017/9/15

Y1 - 2017/9/15

N2 - This article presents an empirical comparison of four models from manual control theory on their ability to model targeting behaviour by human users using a mouse: McRuer's Crossover, Costello's Surge, secondorder lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data.We present the use of time-series, phase space, and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts' law based approaches in HCI, with models providing representations and predictions of human pointing dynamics, which can improve our understanding of pointing and inform design.

AB - This article presents an empirical comparison of four models from manual control theory on their ability to model targeting behaviour by human users using a mouse: McRuer's Crossover, Costello's Surge, secondorder lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data.We present the use of time-series, phase space, and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts' law based approaches in HCI, with models providing representations and predictions of human pointing dynamics, which can improve our understanding of pointing and inform design.

KW - Aimed movements

KW - Control theory

KW - Dynamics

KW - Fitts' law

KW - Modelling

KW - Pointing

KW - Targeting

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U2 - 10.1145/3121431

DO - 10.1145/3121431

M3 - Journal article

AN - SCOPUS:85028679237

VL - 24

JO - A C M Transactions on Computer - Human Interaction

JF - A C M Transactions on Computer - Human Interaction

SN - 1073-0516

IS - 4

M1 - 27

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