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Control theoretic models of pointing

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Müller, Jörg
dc.contributor.author Oulasvirta, Antti
dc.contributor.author Murray-Smith, Roderick
dc.date.accessioned 2018-03-16T10:31:36Z
dc.date.available 2018-03-16T10:31:36Z
dc.date.issued 2017-08-01
dc.identifier.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 en
dc.identifier.issn 1073-0516
dc.identifier.other PURE UUID: 36f61493-6ded-4fb0-88db-95c89096f609
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/36f61493-6ded-4fb0-88db-95c89096f609
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85028679237&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/18148392/a27_muller.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/30254
dc.description | openaire: EC/H2020/637991/EU//COMPUTED
dc.description.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. en
dc.format.extent 36
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/637991/EU//COMPUTED
dc.relation.ispartofseries ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION en
dc.relation.ispartofseries Volume 24, issue 4 en
dc.rights openAccess en
dc.title Control theoretic models of pointing en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Aarhus University
dc.contributor.department Department of Communications and Networking
dc.contributor.department University of Glasgow
dc.subject.keyword Aimed movements
dc.subject.keyword Control theory
dc.subject.keyword Dynamics
dc.subject.keyword Fitts' law
dc.subject.keyword Modelling
dc.subject.keyword Pointing
dc.subject.keyword Targeting
dc.identifier.urn URN:NBN:fi:aalto-201803161724
dc.identifier.doi 10.1145/3121431
dc.type.version publishedVersion


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