Gesture-Based Smartwatch Text Entry: Design, Evaluation, and Future Applications - Horbylon Nascimento, Thamer; U. Fonseca, Afonso; Paula Felix, Juliana; Soares, Fabrizzio (International Journal of Interactive Mobile Technologies (iJIM), 2026)
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 Naïve 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.