Abstract
For people with poor upper limb mobility or control, interaction with a computer may be facilitated by adaptive and alternative interfaces. Visual head tracking has proven to be a viable pointing interface, which can be used when use of the mouse or trackpad is challenging. We are interested in new mechanisms to map the user’s head motion to a pointer location on the screen. Towards this goal, we collected a data set of videos of participants as they were moving their head while following the motion of a marker on the screen. This data set could be used to training a machine learning system for pointing interface. We believe that by learning on real examples, this system may provide a more natural and satisfactory interface than current systems based on pre-defined algorithms.
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Cicek, M., Manduchi, R. (2022). Learning a Head-Tracking Pointing Interface. In: Miesenberger, K., Kouroupetroglou, G., Mavrou, K., Manduchi, R., Covarrubias Rodriguez, M., Penáz, P. (eds) Computers Helping People with Special Needs. ICCHP-AAATE 2022. Lecture Notes in Computer Science, vol 13341. Springer, Cham. https://doi.org/10.1007/978-3-031-08648-9_46
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