Name
Abstract | ||
|---|---|---|
To detect fatigue of a desk worker, this paper focuses on fatigue hidden in smiling and neutral faces and employs a periodic short time monitoring setting. In contrast to continual monitoring, the setting assumes that each short-time monitoring (in this paper, it is called a task) is conducted only during a break time. However, there are two problems: the small number of data in each task and the increasing number of tasks. To detect fatigue, the authors propose a method which is a combination of multi-task learning, clustering and anomaly detection. For the first problem, the authors employ multi-task learning which builds a specific classifier to each task efficiently by using information shared among tasks. Since clustering gathers similar tasks into a cluster, it mitigates the influence of the second problem. Experiments show that the proposed method exhibits a high performance in a long-term monitoring. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26005-1_18 | AMBIENT INTELLIGENCE, AMI 2015 |
Keywords | Field | DocType |
Face monitoring,Anomaly detection,Incremental clustering,Multi-task learning | Small number,Anomaly detection,Multi-task learning,Simulation,Computer science,Cluster analysis,Classifier (linguistics),Periodic graph (geometry),Desk | Conference |
Volume | ISSN | Citations |
9425 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutaka Deguchi | 1 | 22 | 3.66 |
| Einoshin Suzuki | 2 | 853 | 93.41 |