Design of a situation-aware system for abnormal activity detection of elderly people

Authors:
Junbo Wang;Zixue Cheng;Mengqiao Zhang;Yinghui Zhou;Lei Jing
Affiliations:
School of Computer Science and Engineering, The University of Aizu, Japan;School of Computer Science and Engineering, The University of Aizu, Japan;Graduate School of Computer Science and Engineering, The University of Aizu, Japan;Graduate School of Computer Science and Engineering, The University of Aizu, Japan;School of Computer Science and Engineering, The University of Aizu, Japan
Venue:
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Year:
2012

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Abstract

Internet of Things (IoT) is becoming one of hottest research topics. Elderly care is one of important applications in IoT, to grasp the situations around the elder people and then corresponding information can be sent to the care-givers to support the elder people. Abnormal activity detection is a particularly important task in the field, since the services should be immediately provided in such cases. Otherwise the elder people may be in danger. The existing approaches to this problem use some basic living patterns of the elder people, e.g. mobility per day, to detect abnormal activities. However, the detail abnormal activities in various specific situations cannot be detected, e.g., whether there is some abnormal activity when the elder people go to toilet, sleeps or eats something. To solve the above problem, in the paper, we propose a situation-aware abnormality detection system based on SVDD for the elder people. An experiment has been performed focusing on feasibility of the method and accuracy of the system to detect situations and abnormities from real sensors.