Sensors 2014, 14(9), 16181-16195; https://doi.org/10.3390/s140916181
A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors
1
Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea
2
School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK
3
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
(This article belongs to the Special Issue Select Papers from UCAmI & IWAAL 2013 - the 7th International Conference on Ubiquitous Computing and Ambient Intelligence & the 5th International Workshop on Ambient Assisted Living (UCAmI & IWAAL 2013: Pervasive Sensing Solutions)
Abstract
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-TextKeywords:
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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MDPI and ACS Style
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.