Name
Title | ||
|---|---|---|
Automated inference of cognitive performance by fusing multimodal information acquired by smartphone |
Abstract | ||
|---|---|---|
Recognizing human cognitive performance is important for preserving working efficiency and preventing human error. This paper presents a method for estimating cognitive performance by leveraging multiple information available in a smartphone. The method employs the Go-NoGo task to measure cognitive performance, and fuses contextual and behavioral features to identify the level of performance. It was confirmed that the proposed method could recognize whether cognitive performance was high or low with an average accuracy of 71%, even when only referring to inertial sensor logs. Combining sensing modalities improved the accuracy up to 74%.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3346275 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
Go-NoGo task, cognitive performance, machine learning, smartphone log | Computer vision,Inference,Computer science,Human–computer interaction,Artificial intelligence,Effects of sleep deprivation on cognitive performance | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Hamatani | 1 | 12 | 5.27 |
| Keiichi Ochiai | 2 | 4 | 4.10 |
| Akiya Inagaki | 3 | 0 | 0.68 |
| Naoki Yamamoto | 4 | 4 | 3.10 |
| Yusuke Fukazawa | 5 | 137 | 19.28 |
| Masatoshi Kimoto | 6 | 0 | 1.01 |
| Kazuki Kiriu | 7 | 0 | 0.34 |
| Kouhei Kaminishi | 8 | 0 | 0.34 |
| Jun Ota | 9 | 527 | 109.77 |
| Yuri Terasawa | 10 | 0 | 0.68 |
| Tsukasa Okimura | 11 | 3 | 1.79 |
| Takaki Maeda | 12 | 3 | 3.82 |