A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

Sensors

Manhyung Han²  Jae Hun Bang²  Chris Nugent¹  Sally McClean³ 

[1] School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK; E-Mail:;Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea; E-Mails:;School of Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, BT52 1SA, UK; E-Mail:

关键词: activity recognition;    smartphone;    multimodal sensors;    naïve Bayes;    life-log;   

DOI  :  10.3390/s140916181

来源: mdpi

【 摘 要 】

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%.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.