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Sensors 2016, 16(5), 753; doi:10.3390/s16050753

myBlackBox: Blackbox Mobile Cloud Systems for Personalized Unusual Event Detection

1,†,* and 2
1
National Security Research Institute, Yuseong, Daejeon 305-600, Korea
2
Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
Current address: Apt. 520-506, 567 Songpa-daero Songpa-gu, Seoul 05503, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Xue-Bo Jin
Received: 5 March 2016 / Revised: 8 May 2016 / Accepted: 16 May 2016 / Published: 23 May 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
View Full-Text   |   Download PDF [2242 KB, uploaded 23 May 2016]   |  

Abstract

We demonstrate the feasibility of constructing a novel and practical real-world mobile cloud system, called myBlackBox, that efficiently fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users’ daily lives. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve high accuracy, customization, convenience and scalability. We show the feasibility of myBlackBox by implementing and evaluating this end-to-end system that combines Android smartphones with cloud servers, deployed for 15 users over a one-month period. View Full-Text
Keywords: unusual event; mobile user; blackbox; behavior pattern; fusion unusual event; mobile user; blackbox; behavior pattern; fusion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ahn, J.; Han, R. myBlackBox: Blackbox Mobile Cloud Systems for Personalized Unusual Event Detection. Sensors 2016, 16, 753.

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