EdgeSelect: Smartwatch Data Interaction with Minimal Screen Occlusion

Ali Neshati, Aaron Salo, Shariff Am Faleel, Ziming Li, Hai Ning Liang, Celine Latulipe, Pourang Irani

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


We present EdgeSelect, a linear target selection interaction technique that utilizes a small portion of the smartwatch display, explicitly designed to mitigate the 'fat finger' and screen occlusion problems, two of the most common and well-known challenges when interacting with small displays. To design our technique, we first conducted a user study to answer which segments of the smartwatch display have the least screen occlusion while users are interacting with it. We use results from the first experiment to introduce EdgeSelect, a three-layer non-linear interaction technique, which can be used to interact with multiple co-adjacent graphs on the smartwatch by using a region that is the least prone to finger occlusion. In a second experiment, we explore the density limits of the targets possible with EdgeSelect. Finally, we demonstrate the generalizability of EdgeSelect to interact with various types of content.

Original languageEnglish
Title of host publicationICMI 2022 - Proceedings of the 2022 International Conference on Multimodal Interaction
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450393904
Publication statusPublished - 7 Nov 2022
Event24th ACM International Conference on Multimodal Interaction, ICMI 2022 - Bangalore, India
Duration: 7 Nov 202211 Nov 2022

Publication series

NameACM International Conference Proceeding Series


Conference24th ACM International Conference on Multimodal Interaction, ICMI 2022


  • interaction technique
  • smartwatch
  • target selection
  • visualization


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