title: A lightweight hierarchical activity recognition framework using smartphone sensors.
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%.
authors.affiliation: Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea. smiley@oslab.khu.ac.kr.
authors.forename: Manhyung
authors.initials: M
authors.lastname: Han
authors.affiliation: Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea. jhb@oslab.khu.ac.kr.
authors.forename: Jae Hun
authors.initials: JH
authors.lastname: Bang
authors.affiliation: School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK. cd.nugent@ulster.ac.uk.
authors.forename: Chris
authors.initials: C
authors.lastname: Nugent
authors.affiliation: School of Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, BT52 1SA, UK. si.mcclean@ulster.ac.uk.
authors.forename: Sally
authors.initials: S
authors.lastname: McClean
authors.affiliation: Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea. sylee@oslab.khu.ac.kr.
authors.forename: Sungyoung
authors.initials: S
authors.lastname: Lee
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D056044:Actigraphy / Q000295:instrumentation*; D000465:Algorithms; D001185:Artificial Intelligence; D040421:Cell Phone; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D014159:Transducers; D014584:User-Computer Interface