Abstract
Pointing tasks form a significant part of human-computer interaction in graphical user interfaces. Researchers tried to reduce overall pointing time by guessing the intended target a priori from pointer movement characteristics. The task presents challenges due to variability of pointer movements among users and also diversity of applications and target characteristics. Users with age-related or physical impairment makes the task more challenging due to there variable interaction patterns. This paper proposes a set of new models for predicting intended target considering users with and without motor impairment. It also sets up a set of evaluation metrics to compare those models and finally discusses the utilities of those models. Overall we achieved more than 63% accuracy of target prediction in a standard multiple distractor task while our model can recognize the correct target before the user spent 70% of total pointing time, indicating a 30% reduction of pointing time in 63% pointing tasks.
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References
Asano, T., Sharlin, E., Kitamura, Y., Takashima, K., Kishino, F.: Predictive Interaction Using the Delphian Desktop. In: Proceedings of the 186th Annual ACM Smposium on User Interface Software and Technology (UIST 2005), New York, pp. 133–141 (2005)
Bar-Shalom, Y., Li, X.R.: Estimation and Tracking; Principles, Techniques and Software. Artech House (1993)
Biswas, P., Langdon, P.: Developing multimodal adaptation algorithm for mobility impaired users by evaluating their hand strength. International Journal of Human-Computer Interaction 28(9) (2012) ISSN: 1044-7318
Fitts, P.M.: The Information Capacity of The Human Motor System in Controlling The Amplitude of Movement. Journal of Experimental Psychology 47, 381–391 (1954)
Godsill, S., Vermaak, J.: Models and Algorithms For Tracking Using Variable Dimension Particle Filters. In: International Conference on Acoustics, Speech and Signal Processing (2004)
Hwang, F., Keates, S., Langdon, P., Clarkson, P.J.: A submovement analysis of cursor trajectories. Behaviour and Information Technology 3(24), 205–217 (2005)
Keates, S., Hwang, F., Langdon, P., Clarkson, P.J., Robinson, P.: Cursor measures for motion-impaired computer users. In: Proceedings of the Fifth International ACM Conference on Assistive Technologies – ASSETS, New York, pp. 135–142 (2002)
Lane, D.M., Peres, S.C., Sándor, A., Napier, H.A.: A Process for Anticipating and Executing Icon Selection in Graphical User Interfaces. International Journal of Human Computer Interaction 19(2), 243–254 (2005)
Langdon, P.M., Godsill, S., Clarkson, P.J.: Statistical Estimation of User’s Interactions from Motion Impaired Cursor Use Data. In: 6th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT 2006), Esbjerg, Denmark (2006)
Lank, E., Cheng, Y.N., Ruiz, J.: Endpoint prediction using motion kinematics. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2007), New York, NY, USA, pp. 637–646 (2007)
Li, X.R., Jilkov, V.P.: Survey of Maneuvering Target Tracking.Part I: Dynamic Models. IEEE Transactions on Aerospace and Electronic Systems 39(4) (2003)
McGuffin, M.J., Balakrishnan, R.: Fitts’ law and expanding targets: Experimental studies and designs for user interfaces. ACM Transactions Computer-Human Interaction 4(12), 388–422 (2005)
Murata, A.: Improvement of Pointing Time by Predicting Targets in Pointing With a PC Mouse. International Journal of Human-Computer Interaction 10(1), 23–32
Van Oirschot, H.K., Houtsma, A.J.M.: Cursor Trajectory Analysis. In: Brewster, S., Murray-Smith, R. (eds.) Haptic HCI 2000. LNCS, vol. 2058, pp. 127–134. Springer, Heidelberg (2001)
Wobbrock, J.O., Fogarty, J., Liu, S., Kimuro, S., Harada, S.: The Angle Mouse: Target-Agnostic Dynamic Gain Adjustment Based on Angular Deviation. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI 2009), New York, pp. 1401–1410 (2009)
Woodworth, R.S.: The accuracy of voluntary movement. Psychological Review 3, 1–119 (1899)
Ziebart, B., Dey, A., Bagnell, J.A.: Probabilistic pointing target prediction via inverse optimal control. In: Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces (IUI 2012), New York, pp. 1–10 (2012)
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Biswas, P., Aydemir, G.A., Langdon, P., Godsill, S. (2013). Intent Recognition Using Neural Networks and Kalman Filters. In: Holzinger, A., Pasi, G. (eds) Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. HCI-KDD 2013. Lecture Notes in Computer Science, vol 7947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39146-0_11
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DOI: https://doi.org/10.1007/978-3-642-39146-0_11
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