Abstract
This work proposes the development of a method for text entry in smartwatches using continuous gesture recognition, Naïve Bayes classifier and a word dictionary. We performed an evaluation with experts to validate the proposed method. To perform text entry, a user inserts characters through simple gestures, based on geometric shapes, thus, a character is drawn by a user using a set of proposed gestures. We use Naïve Bayes classifier to identify the character that user is entering without the user having to draw it completely. Finally, we use a trie as a dictionary of words to predict words that can be written, considering characters already inserted. We also used the relative probability of word usage in the prediction process. The evaluation with experts showed that it is possible to insert phrases in smartwatches using the proposed method and that the words were inserted correctly.
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References
Bangor, A., Kortum, P., Miller, J.: Determining what individual SUS scores mean: adding an adjective rating scale. J. Usabil. Stud. 4(3), 114–123 (2009). ISSN 1931–3357
John Brooke. Sus: A ‘quick and dirty’ usability scale. In: Usability Evaluation in Industry, pp. 189–194. Taylor and Francis, London, First Edn. (1996)
Brooke, J.: SUS: a retrospective. J. Usabil. Stud. 8(2), 29–40 (2013)
Cha, J.-M., Choi, E., Lim, J.: Virtual sliding qwerty: a new text entry method for smartwatches using tap-n-drag. Appl. Ergon. 51, 263–272 (2015). ISSN 0003-6870. https://doi.org/10.1016/j.apergo.2015.05.008. https://www.sciencedirect.com/science/article/pii/S0003687015000915
Chen, L., Wang, S.: Automated feature weighting in Naive Bayes for high-dimensional data classification. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, pp. 1243–1252. ACM , New York, NY, USA (2012). https://doi.org/10.1145/2396761.2398426. ISBN 978-1-4503-1156-4
Darbar, R., Dash, P., Samanta, D.: Etao keyboard: text input technique on smartwatches. Proc. Comput. Sci. 84, 137–141 (2016). https://doi.org/10.1016/j.procs.2016.04.078, http://www.sciencedirect.com/science/article/pii/S187705091630093X
Gong, J., et al.: Wristext: one-handed text entry on smartwatch using wrist gestures. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, pp. 1–14, New York, NY, USA (2018). Association for Computing Machinery. ISBN 9781450356206. https://doi.org/10.1145/3173574.3173755
Gordon, M., Ouyang, T., Zhai, S., Watchwriter: tap and gesture typing on a smartwatch miniature keyboard with statistical decoding. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 3817–3821, New York, NY, USA (2016). Association for Computing Machinery. ISBN 9781450333627. https://doi.org/10.1145/2858036.2858242
Hong, J., Heo, S., Isokoski, P., Lee, G.: Splitboard: a simple split soft keyboard for wristwatch-sized touch screens. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 1233–1236. Association for Computing Machinery, New York, NY, USA (2015). ISBN 9781450331456. https://doi.org/10.1145/2702123.2702273
Horbylon Nascimento, T., et al.: Using smartwatches as an interactive movie controller: a case study with the Bandersnatch movie. In:L 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 263–268 (2019). https://doi.org/10.1109/COMPSAC.2019.10217
Horbylon Nascimento, T., Soares, F.: Home appliance control using smartwatches with continuous gesture recognition. In: Streitz, N., Konomi, S. (eds.) HCII 2021. LNCS, vol. 12782, pp. 122–134. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77015-0_9
Kristensson, P.O., Denby, L.C.: Continuous recognition and visualization of pen strokes and touch-screen gestures. In: EUROGRAPHICS Symposium on Sketch-Based Interfaces and Modeling (2011)
Lutze, R., Waldhör, K.: Personal health assistance for elderly people via smartwatch based motion analysis. In: 2017 IEEE International Conference on Healthcare Informatics (ICHI), pp. 124–133 (2017). https://doi.org/10.1109/ICHI.2017.79
Nascimento, T.H., Soares, F.A.A.M.N., Irani, P.P., Oliveira, L.L.G., Soares, A.S.: Method for text entry in smartwatches using continuous gesture recognition. In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 549–554 (2017). https://doi.org/10.1109/COMPSAC.2017.168
Nascimento, T.H., et al.: Method for text input with google cardboard: an approach using smartwatches and continuous gesture recognition. In: 2017 19th Symposium on Virtual and Augmented Reality (SVR), pp. 223–226 (2017). https://doi.org/10.1109/SVR.2017.36
Nascimento, T.H., Soares, F.A.A.M.N., Nascimento, H.A.D., Vieira, M.A., Carvalho, T.P., Miranda, W.F.: Netflix control method using smartwatches and continuous gesture recognition. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pp. 1–4 (2019)
Nascimento, T.H., et al.: Interaction with platform games using smartwatches and continuous gesture recognition: a case study. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (2018)
Nebeling, M., et al.: Wearwrite: crowd-assisted writing from smartwatches. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 3834–3846. ACM, New York, NY, USA. (2016). https://doi.org/10.1145/2858036.2858169. http://doi.acm.org/10.1145/2858036.2858169. ISBN 978-1-4503-3362-7
Oney, S., Harrison, C., Ogan, A., Wiese, J.: Zoomboard: a diminutive qwerty soft keyboard using iterative zooming for ultra-small devices. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, pp. 2799–2802. ACM, New York, NY, USA (2013). https://doi.org/10.1145/2470654.2481387. ISBN 978-1-4503-1899-0
Taheri, S., Mammadov, M., Bagirov, A.M.: Improving Naive Bayes classifier using conditional probabilities. In: Proceedings of the Ninth Australasian Data Mining Conference, vol. 121, AusDM 2011, pp. 63–68. Australian Computer Society, Inc., Darlinghurst, Australia, Australia (2011). ISBN 978-1-921770-02-9
Wong, P.C., Zhu, K., Fu, H.: Fingert9: leveraging thumb-to-finger interaction for same-side-hand text entry on smartwatches. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, pp. 1–10. Association for Computing Machinery, New York, NY, USA (2018). ISBN 9781450356206. https://doi.org/10.1145/3173574.3173752
Zaidi, N.A., Cerquides, J., Carman, M.J., Webb, G.I.: Alleviating Naive Bayes attribute independence assumption by attribute weighting. J. Mach. Learn. Res. 14(1), 1947–1988 (2013). ISSN 1532-4435
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Nascimento, T.H., Felix, J.P., Santos Silva, J.L., Soares, F. (2023). Text Entry on Smartwatches Using Continuous Gesture Recognition and Word Dictionary. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14011. Springer, Cham. https://doi.org/10.1007/978-3-031-35596-7_35
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DOI: https://doi.org/10.1007/978-3-031-35596-7_35
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