{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T16:53:40Z","timestamp":1773420820569,"version":"3.50.1"},"reference-count":53,"publisher":"International Association of Online Engineering (IAOE)","issue":"05","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Interact. Mob. Technol."],"abstract":"<jats:p>This work presents an approach for text entry on smartwatches through continuous gesture recognition of geometric shapes. The method allows users to input characters using simple and easily reproducible gestures, such as straight lines and curves, which are recognized in real time as they are performed. A Na\u00efve Bayes classifier categorizes gestures into letters based on conditional probability, and a trie data structure stores words together with their corresponding usage probabilities to enable word suggestion generation during input. The system also incorporates a mechanism that considers both shorter and more frequent words by balancing word length and usage probability. A user evaluation assessed perceived usability using the System Usability Scale (SUS), resulting in an average score of 92.5, which reflects a strong perception of ease of use, low complexity, and rapid learnability. In addition, a quantitative performance analysis indicated an average entry speed of 16.0 words per minute (WPM), providing a complementary characterization of the user interaction behavior. Together, these results indicate that the method provides a feasible interaction approach for devices with limited input space and offers a solid foundation for future studies exploring its applicability in wearable computing, accessibility, and educational contexts.<\/jats:p>","DOI":"10.3991\/ijim.v20i05.58365","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:01:11Z","timestamp":1773414071000},"source":"Crossref","is-referenced-by-count":0,"title":["Gesture-Based Smartwatch Text Entry: Design, Evaluation, and Future Applications"],"prefix":"10.3991","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3509-4700","authenticated-orcid":false,"given":"Thamer","family":"Horbylon Nascimento","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5517-2051","authenticated-orcid":false,"given":"Afonso","family":"U. Fonseca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4095-1639","authenticated-orcid":false,"given":"Juliana","family":"Paula Felix","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1598-1377","authenticated-orcid":false,"given":"Fabrizzio","family":"Soares","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2371","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"38247","doi-asserted-by":"crossref","unstructured":"[1] M. Weiser, \u201cThe computer for the 21st century,\u201d Scienti\ufb01c American, vol. 265, no. 3, pp. 94\u2013105, 1991. [Online]. Available: http:\/\/www.jstor.org\/stable\/24938718.","DOI":"10.1038\/scientificamerican0991-94"},{"key":"38249","doi-asserted-by":"crossref","unstructured":"[2] H. A. Almusawi, C. M. Durugbo, and A. M. Bugawa, \u201cWearable Technology in Education: A Systematic Review,\u201d IEEE Transactions on Learning Technologies, vol. 14, no. 4, pp. 540\u2013554, 2021, doi: 10.1109\/TLT.2021.3107459.","DOI":"10.1109\/TLT.2021.3107459"},{"key":"38251","doi-asserted-by":"crossref","unstructured":"[3] R. Aloqlah, \u201cExploring AI-Powered Mobile Technologies in Educational Leadership: Perceptions, Challenges, and Opportunities,\u201d International Journal of Interactive Mobile Technologies (iJIM), vol. 19, no. 13, pp. 78\u201395, Jul. 2025, doi: 10.3991\/ijim.v19i13.53081.","DOI":"10.3991\/ijim.v19i13.53081"},{"key":"38253","doi-asserted-by":"crossref","unstructured":"[4] T. Horbylon Nascimento, C. B. R. Ferreira, W. G. Rodrigues, and F. Soares, \u201cInteraction with smartwatches using gesture recognition: A systematic literature review,\u201d in 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 2020, pp. 1661\u20131666.","DOI":"10.1109\/COMPSAC48688.2020.00-17"},{"key":"38255","doi-asserted-by":"crossref","unstructured":"[5] R. Lutze and K. Waldho\u00a8r, \u201cPersonal health assistance for elderly people via smartwatch based motion analysis,\u201d in 2017 IEEE International Conference on Healthcare Informatics (ICHI), 8 2017, pp. 124\u2013133.","DOI":"10.1109\/ICHI.2017.79"},{"key":"38257","doi-asserted-by":"crossref","unstructured":"[6] D. Xuanfeng, \u201cGesture recognition and response system for special education using computer vision and human\u2013computer interaction technology,\u201d Disability and Rehabilitation: Assistive Technology, pp. 1\u201318, 2025, doi: 10.1080\/17483107.2025.2527226.","DOI":"10.1080\/17483107.2025.2527226"},{"key":"38259","unstructured":"[7] S. 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