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Sensors 2014, 14(9), 16181-16195;

A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea
School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK
School of Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, BT52 1SA, UK
Author to whom correspondence should be addressed.
Received: 6 April 2014 / Revised: 22 August 2014 / Accepted: 26 August 2014 / Published: 2 September 2014
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Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user’s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. View Full-Text
Keywords: activity recognition; smartphone; multimodal sensors; naïve Bayes; life-log activity recognition; smartphone; multimodal sensors; naïve Bayes; life-log
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Han, M.; Bang, J.H.; Nugent, C.; McClean, S.; Lee, S. A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors. Sensors 2014, 14, 16181-16195.

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