Interruptibility Estimation Based on Head Motion and PC Operation

Abstract

Frequent and uncontrolled interruptions by information systems that do not reflect the user’s state can result in fragmented working times and decreased intellectual productivity. To avoid adverse interruptions, interruptibility estimation methods based on PC operation information have been proposed. However, workers who use PCs to accomplish their primary tasks occasionally engage in paperwork. Occasional paperwork activities, which are not reflected in the PC’s operation information, can cause estimation errors. This study focuses on using the position of the head, posture, temporal motion, and continuity of the head position and posture while a worker is at his or her desk as indices to reflect engagement in the task at hand. Based on an analysis of the relationship between the head-related parameters and interruptibility, an interruptibility estimation algorithm is proposed using four head-related indices that reflect interruptibility during PC and non-PC work. Experiments indicate that estimation accuracy improves as a result of incorporating these indices in the algorithm.

Additional information

Funding

This work was partly supported by the Japan Society for the Promotion of Science (KAKENHI); the Ministry of Education, Culture, Sports, Science and Technology; Japan Fund for Smart Space Technology toward Sustainable Society; and the National Institute of Information and Communications Technology, commissioned research.

Notes on contributors

Takahiro Tanaka

Takahiro Tanaka, Ph.D., is an Assistant Professor at the Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology. His research interests include intelligent agents, user state estimation, human-agent interaction, and human-computer interaction.

Ryosuke Abe

Ryosuke Abe graduated from the Department of Computer and Information Sciences at Tokyo University of Agriculture and Technology in 2012 and received a Master of Engineering degree. His research interest is user state estimation from head motion during work.

Kazuaki Aoki

Kazuaki Aoki, Ph.D., is an Assistant Professor at the Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology. His research interests include pattern recognition, machine learning, user state estimation, and human interfaces.

Kinya Fujita

Kinya Fujita, Ph.D., is a Professor at the Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology. His research interests are smart human–computer interfaces, communication in virtual space, and haptic virtual reality.