Name
Abstract | ||
|---|---|---|
As the size of smartphone touchscreens becomes larger and larger in recent years, operability with single hand is getting worse especially for female users. We envision that user experience can be significantly improved if smartphones are able to detect the current operating hand and adjust the UI subsequently. In this paper, we propose a novel scheme that leverages user-generated touchscreen traces to recognize current operating hand accurately, with the help of a supervised classifier constructed from twelve different kinds of touchscreen trace features. As opposed to existing solutions that all require users to select the current operating hand or dominant hand manually, our scheme follows a more convenient and practical manner, and allows users to change operating hand frequently without any harm to user experience. We conduct a series of real-world experiments on Samsung Galaxy S4 smartphones, and evaluation results demonstrate that our proposed approach achieves 94.1% accuracy when deciding with a single trace only, and the false positive rate is as low as 2.6%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25159-2_19 | KSEM |
Keywords | Field | DocType |
Operating hand recognition,Smartphone touchscreen,User interface adjustment,Supervised classification | False positive rate,User experience design,Computer science,Touchscreen,Operability,Human–computer interaction,Artificial intelligence,Classifier (linguistics),Machine learning,Embedded system | Conference |
Volume | ISSN | Citations |
9403 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 7 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hansong Guo | 1 | 4 | 1.77 |
| He Huang | 2 | 829 | 65.14 |
| Zehao Sun | 3 | 19 | 4.07 |
| Liusheng Huang | 4 | 1082 | 123.52 |
| Zhenyu Zhu | 5 | 9 | 1.89 |
| Shaowei Wang | 6 | 1119 | 85.65 |
| Pengzhan Wang | 7 | 28 | 5.53 |
| Hongli Xu | 8 | 502 | 85.92 |
| Hengchang Liu | 9 | 381 | 34.84 |