Hidden fatigue detection for a desk worker using clustering of successive tasks

Yutaka Deguchi, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAmbient Intelligence - 12th European Conference, AmI 2015, Proceedings
EditorsAchilles Kameas, Irene Mavrommati, Boris De Ruyter, Periklis Chatzimisios
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319260044
Publication statusPublished - 2015
Event12th European Conference on Ambient Intelligence, AmI 2015 - Athens, Greece
Duration: Nov 11 2015Nov 13 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th European Conference on Ambient Intelligence, AmI 2015

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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