Leveraging finger identification to integrate multi-touch command selection and parameter manipulation

Alix Goguey 1 Daniel Vogel 2 Fanny Chevalier 1 Thomas Pietrzak 3, 1 Nicolas Roussel 1 Géry Casiez 1, 3
1 MJOLNIR - Computing tools to empower users
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : Identifying which fingers are touching a multi-touch surface provides a very large input space. We describe FingerCuts, an interaction technique inspired by desktop keyboard shortcuts to exploit this potential. FingerCuts enables integrated command selection and parameter manipulation, it uses feed-forward and feedback to increase discoverability, it is backward compatible with current touch input techniques, and it is adaptable for different touch device form factors. We implemented three variations of FingerCuts, each tailored to a different device form factor: tabletop, tablet, and smartphone. Qualitative and quantitative studies conducted on the tabletop suggests that with some practice, FingerCuts is expressive, easy-to-use, and increases a sense of continuous interaction flow and that interaction with FingerCuts is as fast, or faster than using a graphical user interface. A theoretical analysis of FingerCuts using the Fingerstroke-Level Model (FLM) matches our quantitative study results, justifying our use of FLM to analyse and validate the performance for the other device form factors.
Type de document :
Article dans une revue
International Journal of Human-Computer Studies, Elsevier, 2017, 99, pp.21-36. 〈http://www.sciencedirect.com/〉. 〈10.1016/j.ijhcs.2016.11.002〉
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https://hal.inria.fr/hal-01558712
Contributeur : Fanny Chevalier <>
Soumis le : dimanche 9 juillet 2017 - 14:31:33
Dernière modification le : vendredi 8 septembre 2017 - 14:14:26

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Alix Goguey, Daniel Vogel, Fanny Chevalier, Thomas Pietrzak, Nicolas Roussel, et al.. Leveraging finger identification to integrate multi-touch command selection and parameter manipulation. International Journal of Human-Computer Studies, Elsevier, 2017, 99, pp.21-36. 〈http://www.sciencedirect.com/〉. 〈10.1016/j.ijhcs.2016.11.002〉. 〈hal-01558712〉

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