J. Alberto Álvarez Martín
Jörg Müller
Roderick Murray-Smith
Skip Abstract Section
Skip Supplemental Material SectionAbstract
We present Intermittent Control (IC) models as a candidate framework for modelling human input movements in Human–Computer Interaction (HCI). IC differs from continuous control in that users are not assumed to use feedback to adjust their movements continuously, but only when the difference between the observed pointer position and predicted pointer positions becomes large. We use a parameter optimisation approach to identify the parameters of an intermittent controller from experimental data, where users performed one-dimensional mouse movements in a reciprocal pointing task. Compared to previous published work with continuous control models, based on the Kullback–Leibler divergence from the experimental observations, IC is better able to generatively reproduce the distinctive dynamical features and variability of the pointing task across participants and over repeated tasks. IC is compatible with current physiological and psychological theory and provides insight into the source of variability in HCI tasks.
Supplemental Material
Available for Download
zip
martin.zip (898.6 KB)
Supplemental movie, appendix, image and software files for, Intermittent Control as a Model of Mouse Movements
- Johnny Accot and Shumin Zhai. 1997. Beyond Fitts’ law: Models for trajectory-based HCI tasks. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 250. Google Scholar
Digital Library
- Johnny Accot and Shumin Zhai. 2002. More than dotting the i’s—foundations for crossing-based interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 73–80. Google ScholarDigital Library
- S. Aranovskiy, R. Ushirobira, D. Efimov, and G. Casiez. 2016. Modeling pointing tasks in mouse-based human-computer interactions. In Proceedings of the 2016 IEEE 55th Conference on Decision and Control. IEEE, Las Vegas, NV, 6595–6600.Google Scholar
- Stanislav Aranovskiy, Rosane Ushirobira, Denis Efimov, and Géry Casiez. 2020. A switched dynamic model for pointing tasks with a computer mouse. Asian Journal of Control 22, 4 (2020), 1387–1400.Google ScholarDigital Library
- Myroslav Bachynskyi. 2016. Biomechanical Models for Human-Computer Interaction. Ph.D. Dissertation. Universität des Saarlandes Saarbrücken.Google Scholar
- Myroslav Bachynskyi, Gregorio Palmas, Antti Oulasvirta, and Tino Weinkauf. 2015. Informing the design of novel input methods with muscle coactivation clustering. ACM Transactions on Computer-Human Interaction 21, 6 (2015), 1–25. Google ScholarDigital Library
- Ravin Balakrishnan. 2004. ‘Beating’ Fitts’ law: Virtual enhancements for pointing facilitation. International Journal of Human-Computer Studies 61, 6 (2004), 857–874. Google ScholarDigital Library
- R. C. Barrett, E. J. Selker, J. D. Rutledge, and R. S. Olyha. 1995. Negative inertia: A dynamic pointing function. In Proceedings of the Conference Companion on Human Factors in Computing Systems. ACM, New York, NY, 316–317. Google ScholarDigital Library
- Paul M. Bays and Daniel M. Wolpert. 2007. Computational principles of sensorimotor control that minimize uncertainty and variability. The Journal of Physiology 578, 2 (2007), 387–396.Google ScholarCross Ref
- Nikhil Bhushan and Reza Shadmehr. 1999. Computational nature of human adaptive control during learning of reaching movements in force fields. Biological Cybernetics 81, 1 (1999), 39–60.Google ScholarCross Ref
- Magali Billon, Reinoud J. Bootsma, and Denis Mottet. 2000. The dynamics of human isometric pointing movements under varying accuracy requirements. Neuroscience Letters 286, 1 (2000), 49–52.Google ScholarCross Ref
- Renaud Blanch, Yves Guiard, and Michel Beaudouin-Lafon. 2004. Semantic pointing: Improving target acquisition with control-display ratio adaptation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 519–526. Google ScholarDigital Library
- R. J. Bootsma, L. Fernandez, and D. Mottet. 2004. Behind Fitts’ law: Kinematic patterns in goal-directed movements. International Journal of Human-Computer Studies 61, 6 (2004), 811–821. Google ScholarDigital Library
- Robin T. Bye and Peter D. Neilson. 2008. The BUMP model of response planning: Variable horizon predictive control accounts for the speed–accuracy tradeoffs and velocity profiles of aimed movement. Human Movement Science 27, 5 (2008), 771–798.Google ScholarCross Ref
- S. K. Card, T. P. Moran, and A. Newell. 1986. The model human processor: An engineering model for human performance. In Handbook of Perception and Human Performance, Vol. 2. Cognitive Processes and Performance. K. R. Boff, L. Kaufman, & J. P. Thomas (Eds.), Wiley, pp. 1–35. Google Scholar
- Géry Casiez and Nicolas Roussel. 2011. No more bricolage! Methods and tools to characterize, replicate and compare pointing transfer functions. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology. ACM, New York, NY, 603–614. Google ScholarDigital Library
- Géry Casiez, Daniel Vogel, Ravin Balakrishnan, and Andy Cockburn. 2008. The impact of control-display gain on user performance in pointing tasks. Human–Computer Interaction 23, 3 (2008), 215–250.Google Scholar
- Olivier Chapuis, Renaud Blanch, and Michel Beaudouin-Lafon. 2007. Fitts’ Law in the Wild: A Field Study of Aimed Movements. LRI Technical Repport Number 1480. Univ. Paris-Sud, Orsay.Google Scholar
- Sung-Jung Cho, Roderick Murray-Smith, and Yeun-Bae Kim. 2007. Multi-context photo browsing on mobile devices based on tilt dynamics. In Proceedings of the 9th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, New York, NY, 190–197. Google ScholarDigital Library
- Kenneth J. W. Craik. 1947. Theory of the human operator in control systems. I. the operator as an engineering system. British Journal of Psychology 38, 2 (1947), 56–61. Google Scholar
- Kenneth J. W. Craik. 1948. Theory of the human operator in control systems; man as an element in a control system.The British Journal of Psychology. General Section 38, 3 (1948), 142–148. Google ScholarCross Ref
- E. R. F. W. Crossman and P. J. Goodeve. 1983. Feedback control of hand-movement and Fitts’ law. The Quarterly Journal of Experimental Psychology 35, 2 (1983), 251–278.Google ScholarCross Ref
- Antonia F. de C. Hamilton, Kelvin E. Jones, and Daniel M. Wolpert. 2004. The scaling of motor noise with muscle strength and motor unit number in humans. Experimental Brain Research 157, 4 (Aug. 2004), 417–430. Google Scholar
- Ashesh K. Dhawale, Maurice A. Smith, and Bence P. Ölveczky. 2017. The role of variability in motor learning. Annual Review of Neuroscience 40, 1 (2017), 479–498.Google ScholarCross Ref
- Parisa Eslambolchilar and Roderick Murray-Smith. 2004. Tilt-based automatic zooming and scaling in mobile devices – A state-space implementation. In Mobile Human-Computer Interaction - MobileHCI 2004. S. Brewster and M. Dunlop (Eds.). Springer Berlin Heidelberg, Berlin, 120–131. Google ScholarCross Ref
- Parisa Eslambolchilar and Roderick Murray-Smith. 2006. Model-based, multimodal interaction in document browsing. In Proceedings of the 3rd International Conference on Machine Learning for Multimodal Interaction. Springer-Verlag, Berlin, 1–12. Google ScholarDigital Library
- Parisa Eslambolchilar and Roderick Murray-Smith. 2008. Control centric approach in designing scrolling and zooming user interfaces. International Journal of Human-Computer Studies 66, 12 (2008), 838–856. Google ScholarDigital Library
- A. Aldo Faisal and Simon B. Laughlin. 2007. Stochastic simulations on the reliability of action potential propagation in thin axons. PLOS Computational Biology 3, 5 (May 2007), 1–13.Google Scholar
- A. Aldo Faisal, Luc P. J. Selen, and Daniel M. Wolpert. 2008. Noise in the nervous system. Nature Reviews Neuroscience 9, 4 (April 2008), 292–303. Google ScholarCross Ref
- Jean-Daniel Fekete, Niklas Elmqvist, and Yves Guiard. 2009. Motion-pointing: Target selection using elliptical motions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 289–298. Google ScholarDigital Library
- Florian Fischer, Miroslav Bachinski, Markus Klar, Arthur Fleig, and Jörg Müller. 2020. Reinforcement learning control of a biomechanical model of the upper extremity. arxiv:2011.07105. Retrieved from https://arxiv.org/abs/2011.07105.Google Scholar
- Paul M. Fitts. 1954. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47, 6 (1954), 381–391.Google ScholarCross Ref
- U. Forssell and L. Ljung. 1999. Closed-loop identification revisited. Automatica 35, 7 (1999), 1215–1241. Google ScholarDigital Library
- P. Gawthrop, H. Gollee, and I. Loram. 2015. Intermittent control in man and machine. In Event-Based Control and Signal Processing, M. Miskowicz (Ed.). CRC, London, Chapter 14, 1–99.Google Scholar
- P. Gawthrop, I. Loram, H. Gollee, and M. Lakie. 2014. Intermittent control models of human standing: Similarities and differences.Biological Cybernetics 108, 2 (April 2014), 159–68. Google ScholarDigital Library
- Peter Gawthrop, Ian Loram, Martin Lakie, and Henrik Gollee. 2011. Intermittent control: A computational theory of human control. Biological Cybernetics 104, 1–2 (2011), 31–51. Google ScholarDigital Library
- P. Gawthrop and L. Wang. 2007. Intermittent model predictive control. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, 7 (2007), 1007–1018.Google ScholarCross Ref
- P. Gawthrop and L. Wang. 2011. The system-matched hold and the intermittent control separation principle. International Journal of Control 84, 12 (2011), 1965–1974.Google ScholarCross Ref
- Peter J. Gawthrop and Liuping Wang. 2008. Towards model-based continuous-time identification of the human balance controller. IFAC Proceedings Volumes 41, 2 (2008), 11612–11617.Google Scholar
- Peter J. Gawthrop and Liuping Wang. 2009. Event-driven intermittent control. International Journal of Control 82, 12 (2009), 2235–2248.Google ScholarCross Ref
- Henrik Gollee, Peter J. Gawthrop, Martin Lakie, and Ian D. Loram. 2017. Visuo-manual tracking: Does intermittent control with aperiodic sampling explain linear power and non-linear remnant without sensorimotor noise?The Journal of Physiology 595, 21 (2017), 6751–6770.Google Scholar
- G. C. Goodwin, S. F. Graebe, and M. E. Salgado. 2001. Control System Design. Prentice Hall, New Jersey, NJ. Google ScholarDigital Library
- Julien Gori. 2018. Modeling the Speed-Accuracy Tradeoff Using the Tools of Information Theory. Ph.D. Dissertation. Paris Saclay.Google Scholar
- Julien Gori, Olivier Rioul, and Yves Guiard. 2018. Speed-accuracy tradeoff: A Formal Information-Theoretic Transmission Scheme (FITTS). ACM Transactions on Computer-Human Interaction 25, 5 (2018), 1–33. Google ScholarDigital Library
- Yves Guiard. 1993. On Fitts’s and Hooke’s laws: Simple harmonic movement in upper-limb cyclical aiming. Acta Psychologica 82, 1 (1993), 139–159.Google ScholarCross Ref
- Yves Guiard and Michel Beaudouin-Lafon. 2004a. Fitts’ law 50 years later: Applications and contributions from human-computer interaction. International Journal of Human-Computer Studies 61, 6 (2004), 747–750. Google ScholarDigital Library
- Y. Guiard and M. Beaudouin-Lafon. 2004b. Target acquisition in multiscale electronic worlds.International Journal of Human-Computer Studies 61, 6 (2004), 875–905. Google ScholarDigital Library
- Erik Hollnagel. 1999. Modelling the controller of a process. Transactions of the Institute of Measurement and Control 21, 4–5 (1999), 163–170.Google Scholar
- E. Hollnagel and D. D Woods. 2005. Joint Cognitive Systems: Foundations of Cognitive Systems Engineering. CRC, Boca Raton, FL. Google ScholarDigital Library
- R. J. Jagacinski. 1977. A qualitative look at feedback control theory as a style of describing behavior. Human Factors: The Journal of the Human Factors and Ergonomics Society 19, 4 (1977), 331–347.Google ScholarCross Ref
- Richard J. Jagacinski and John M. Flach. 2003. Control Theory for Humans: Quantitative Approaches to Modeling Performance. Lawrence Erlbaum, Mahwah, New Jersey, NJ.Google Scholar
- Kelvin E. Jones, Antonia F. de C. Hamilton, and Daniel M. Wolpert. 2002. Sources of signal-dependent noise during isometric force production. Journal of Neurophysiology 88, 3 (Sept. 2002), 1533–1544.Google ScholarCross Ref
- David L. Kleinman. 1969. Optimal control of linear systems with time-delay and observation noise. IEEE Transactions on Automatic Control 14, 5 (1969), 524–527. Google ScholarCross Ref
- D. L. Kleinman, S. Baron, and W. H. Levison. 1970. An optimal control model of human response part I: Theory and validation. Automatica 6, 3 (1970), 357–369. Google ScholarDigital Library
- Sven Kratz, Ivo Brodien, and Michael Rohs. 2010. Semi-automatic zooming for mobile map navigation. In Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, New York, NY, 63–72. Google ScholarDigital Library
- W. H. Levison, S. Baron, and D. L. Kleinman. 1969. A model for human controller remnant. IEEE Transactions on Man-Machine Systems 10, 4 (1969), 101–108. Google ScholarCross Ref
- Ian Loram, Peter Gawthrop, and Henrik Gollee. 2015. Intermittent control of unstable multivariate systems. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1436–1439.Google ScholarCross Ref
- Ian D. Loram, Peter J. Gawthrop, and Martin Lakie. 2006. The frequency of human, manual adjustments in balancing an inverted pendulum is constrained by intrinsic physiological factors. The Journal of Physiology 577, 1 (2006), 417–432.Google ScholarCross Ref
- Ian D. Loram, Henrik Gollee, Martin Lakie, and Peter J. Gawthrop. 2011. Human control of an inverted pendulum: iIs continuous control necessary? Is intermittent control effective? Is intermittent control physiological?The Journal of Physiology 589, 2 (2011), 307–324.Google Scholar
- I. D. Loram and M. Lakie. 2002. Human balancing of an inverted pendulum: Position control by small, ballistic-like, throw and catch movements. The Journal of Physiology 540, 3 (March 2002), 1111–1124. Google ScholarCross Ref
- I. D. Loram, Cornelis van de Kamp, H. Gollee, and P. J. Gawthrop. 2012. Identification of intermittent control in man and machine.Journal of the Royal Society Interface 9, 74 (Sept. 2012), 2070–84. Google Scholar
- Ian David Loram, Cornelis van De Kamp, Martin Lakie, Henrik Gollee, and Peter J. Gawthrop. 2014. Does the motor system need intermittent control?Exercise and Sport Sciences Reviews 42, 3 (2014), 117–125.Google Scholar
- I. Scott MacKenzie. 1992. Fitts’ law as a research and design tool in human–computer interaction. Human-Computer Interaction 7, 1 (1992), 91–139. Google ScholarDigital Library
- Leland McInnes, John Healy, and James Melville. 2018. UMAP: Uniform Manifold Approximation and Projection for dimension reduction. arxiv:1802.03426. Retrieved from https://arxiv.org/abs/1802.03426.Google Scholar
- D. E. Meyer, R. Abrams, S. Kornblum, C. E. Wright, and J. E. Smith. 1988. Optimality in human motor performance: Ideal control of rapid aimed movements.Psychological Review 95, 3 (1988), 340–370. Google Scholar
- D. E. Meyer, J. E. Keith-Smith, S. Kornblum, R. A. Abrams, and C. E. Wright. 1990. Speed-accuracy trade-offs in aimed movements: Toward a theory of rapid voluntary action. In Attention and Performance XIII: Motor Representation and Control. M. Jeannerod (Ed.), Lawrence Erlbaum Associates, Inc, New Jersey, NJ, 173–226.Google Scholar
- R. Christopher Miall. 1986. Simple or complex systems?Behavioral and Brain Sciences 9, 4 (1986), 734–734.Google Scholar
- R. C. Miall, D. Jo Weir, Daniel M. Wolpert, and J. F. Stein. 1993. Is the cerebellum a smith predictor?Journal of Motor Behavior 25, 3 (1993), 203–216.Google Scholar
- H. S. Milner-Brown, R. B. Stein, and R. Yemm. 1973. The contractile properties of human motor units during voluntary isometric contractions. The Journal of Physiology 228, 2 (1973), 285–306.Google ScholarCross Ref
- Jörg Müller, Antti Oulasvirta, and Roderick Murray-Smith. 2017. Control theoretic models of pointing. ACM Transactions on Computer-Human Interaction 24, 4, Article 27 (Aug. 2017), 36 pages. Google ScholarDigital Library
- Roderick Murray-Smith. 2018. Control theory, dynamics and continuous interaction. In Computational Interaction. A. Oulasvirta, P. O. Kristensson, X. Bi, and A. Howes (Eds.), Oxford University Press, Oxford, 17–42.Google Scholar
- T. Navas and L. Stark. 1968. Sampling or intermittency in hand control system dynamics. Biophysics Journal 8, 2 (1968), 252–302.Google ScholarCross Ref
- P. D. Nielson. 1999. Influence of lntermittency and Synergy on Grasping. Motor Control 3, 3 (1999), 280–284.Google ScholarCross Ref
- Y. Oytam, P. D. Neilson, and N. O’Dwyer. 2005. Degrees of freedom and motor planning in purposive movement. Human Movement Science 24, 5–6 (2005), 710–730.Google ScholarCross Ref
- Eunji Park and Byungjoo Lee. 2020. An intermittent click planning model. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 1–13. Google ScholarDigital Library
- Emanuel Parzen. 1962. On estimation of a probability density function and model. Annals of Mathematical Statistics 33, 3 (1962), 1065–1076.Google ScholarCross Ref
- E. C. Poulton. 1974. Tracking Skill and Manual Control. Academic Press, New York, NY.Google Scholar
- Philip Quinn, Sylvain Malacria, and Andy Cockburn. 2013. Touch scrolling transfer functions. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology. ACM, New York, NY, 61–70. Google ScholarDigital Library
- Philip Quinn and Shumin Zhai. 2018. Modeling gesture-typing movements. Human–Computer Interaction 33, 3 (2018), 234–280.Google Scholar
- E. Ronco, T. Arsan, and P. J. Gawthrop. 1999. Open-loop intermittent feedback control: Practical continuous-time GPC. IEE Proceedings-Control Theory and Applications 146, 5 (1999), 426–434.Google ScholarCross Ref
- Murray Rosenblatt. 1956. Remarks on some nonparametric estimates of a density function. Annals of Mathematical Statistics 27, 3 (1956), 832–837.Google ScholarCross Ref
- Richard A. Schmidt, Howard N. Zelaznik, and James S. Frank. 1978. Sources of inaccuracy in rapid movement. In Information Processing in Motor Control and Learning. George E. Stelmach (Ed.), Academic Press, New York, NY, 183–203. Google Scholar
- Sofia Seinfeld, Tiare Feuchtner, Antonella Maselli, and Jörg Müller. 2020. User representations in human-computer interaction. Human–Computer Interaction 0, 0 (2020), 1–39.Google Scholar
- Reza Shadmehr and Sandro Mussa-Ivaldi. 2012. Biological Learning and Control: How the Brain Builds Representations, Predicts Events, and Makes Decisions. MIT Press, Cambridge, MA.Google Scholar
- R. William Soukoreff and I. Scott MacKenzie. 2004. Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts’ law research in HCI. International Journal of Human-Computer Studies 61, 6 (2004), 751–789. Google ScholarDigital Library
- C. W. Telford. 1931. The refractory phase of voluntary and associative responses. Journal of Experimental Psychology 14, 1 (1931), 1–36.Google ScholarCross Ref
- Virginia Torczon. 1997. On the convergence of pattern search algorithms. SIAM Journal on Optimization 7, 1 (1997), 1–25. Google ScholarDigital Library
- Dari Trendafilov and Roderick Murray-Smith. 2013. Information-theoretic characterization of uncertainty in manual control. In Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE Computer Society, 4913–4918. Google ScholarDigital Library
- Cornelis van de Kamp, P. J. Gawthrop, H. Gollee, and I. D. Loram. 2013. Refractoriness in sustained visuo-manual control: Is the refractory duration intrinsic or does it depend on external system properties?PLoS Comput Biol 9, 1 (Jan. 2013), e1002843. Google Scholar
- Herman Van Der Kooij and Erwin De Vlugt. 2007. Postural responses evoked by platform pertubations are dominated by continuous feedback. Journal of Neurophysiology 98, 2 (2007), 730–743.Google ScholarCross Ref
- Herman Van Der Kooij and Robert J. Peterka. 2011. Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise. Journal of Computational Neuroscience 30, 3 (2011), 759–778. Google ScholarDigital Library
- Eduardo Velloso, Marcus Carter, Joshua Newn, Augusto Esteves, Christopher Clarke, and Hans Gellersen. 2017. Motion correlation: Selecting objects by matching their movement. ACM Transactions on Computer-Human Interaction 24, 3 (2017), 1–35. Google ScholarDigital Library
- Qing Wang, Sanjeev R. Kulkarni, and Sergio Verdu. 2009. Divergence estimation for multidimensional densities via $k$-Nearest-Neighbor distances. IEEE Transactions on Information Theory 55, 5 (May 2009), 2392–2405. Google ScholarDigital Library
- John Williamson and Roderick Murray-Smith. 2004. Pointing without a pointer. In Proceedings of the CHI’04 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, NY, 1407–1410. Google ScholarDigital Library
- John Williamson, Roderick Murray-Smith, and Stephen Hughes. 2007. Shoogle: Excitatory multimodal interaction on mobile devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 121–124. Google ScholarDigital Library
- Jacob O. Wobbrock, Edward Cutrell, Susumu Harada, and I. Scott MacKenzie. 2008. An error model for pointing based on Fitts’ law. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 1613–1622. Google ScholarDigital Library
- Daniel M. Wolpert and Zoubin Ghahramani. 2000. Computational principles of movement neuroscience. Nature Neuroscience 3, 11 (2000), 1212–1217.Google ScholarCross Ref
- Daniel M. Wolpert, R. Chris Miall, and Mitsuo Kawato. 1998. Internal models in the cerebellum. Trends in Cognitive Sciences 2, 9 (1998), 338–347.Google ScholarCross Ref
- Howard G. Wu, Yohsuke R. Miyamoto, Luis Nicolas Gonzalez Castro, Bence P. Ölveczky, and Maurice A. Smith. 2014. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nature Neuroscience 17, 2 (2014), 312.Google ScholarCross Ref
Index Terms
- Intermittent Control as a Model of Mouse Movements
Recommendations
Intermittent Control in Autonomous Vehicle Steering Control and Lane Keeping
AIR '21: Proceedings of the 2021 5th International Conference on Advances in RoboticsIntermittent control is a control approach in which a continuous control law is applied, but is switched on and off based on a threshold criteria in the controlled variable. Evidence of intermittency in control strategies employed by humans in various ...
Comparing cursor orientations for mouse, pointer, and pen interaction
CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsMost graphical user interfaces provide visual cursors to facilitate interaction with input devices such as mice, pointers, and pens. These cursors often include directional cues that could influence the stimulus-response compatibility of user input. We ...
Look and lean: accurate head-assisted eye pointing
ETRA '14: Proceedings of the Symposium on Eye Tracking Research and ApplicationsCompared to the mouse, eye pointing is inaccurate. As a consequence, small objects are difficult to point by gaze alone. We suggest using a combination of eye pointing and subtle head movements to achieve accurate hands-free pointing in a conventional ...
Comments