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Open AccessArticle

Activity Recognition Using Gazed Text and Viewpoint Information for User Support Systems

Graduate School of Engineering, Tohoku University, Aoba 6-6-05, Aramaki, Aoba-ku, Sendai 980-8579, Japan
Author to whom correspondence should be addressed.
Current address: Future Architect, Inc., 1-2-2 Osaki, Shinagawa-ku, Tokyo 141-0032, Japan.
J. Sens. Actuator Netw. 2018, 7(3), 31;
Received: 30 June 2018 / Revised: 29 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Wireless Sensor and Actuator Networks for Smart Cities)
The development of information technology has added many conveniences to our lives. On the other hand, however, we have to deal with various kinds of information, which can be a difficult task for elderly people or those who are not familiar with information devices. A technology to recognize each person’s activity and providing appropriate support based on that activity could be useful for such people. In this paper, we propose a novel fine-grained activity recognition method for user support systems that focuses on identifying the text at which a user is gazing, based on the idea that the content of the text is related to the activity of the user. It is necessary to keep in mind that the meaning of the text depends on its location. To tackle this problem, we propose the simultaneous use of a wearable device and fixed camera. To obtain the global location of the text, we perform image matching using the local features of the images obtained by these two devices. Then, we generate a feature vector based on this information and the content of the text. To show the effectiveness of the proposed approach, we performed activity recognition experiments with six subjects in a laboratory environment. View Full-Text
Keywords: activity recognition; eye tracker; fisheye camera; viewpoint information activity recognition; eye tracker; fisheye camera; viewpoint information
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MDPI and ACS Style

Chiba, S.; Miyazaki, T.; Sugaya, Y.; Omachi, S. Activity Recognition Using Gazed Text and Viewpoint Information for User Support Systems. J. Sens. Actuator Netw. 2018, 7, 31.

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