Name
Abstract | ||
|---|---|---|
ABSTRACT 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3536221.3556586 | Multimodal Interfaces and Machine Learning for Multimodal Interaction |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Neshati | 1 | 0 | 0.34 |
| Aaron Salo | 2 | 0 | 0.34 |
| Shariff Am Faleel | 3 | 0 | 0.34 |
| Ziming Li | 4 | 0 | 0.34 |
| Hai-Ning Liang | 5 | 0 | 0.34 |
| Celine Latulipe | 6 | 0 | 0.34 |
| Pourang Irani | 7 | 0 | 0.34 |