Control theoretic models of pointing
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Journal Title
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2017-08-01
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Language
en
Pages
36
Series
ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION, Volume 24, issue 4
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.Description
| openaire: EC/H2020/637991/EU//COMPUTED
Keywords
Aimed movements, Control theory, Dynamics, Fitts' law, Modelling, Pointing, Targeting
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Citation
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
