Activity Recognition Based on RFID Object Usage for Smart Mobile Devices

被引:1
作者
Jaeyoung Yang [1 ]
Joonwhan Lee [2 ]
Joongmin Choi [3 ]
机构
[1] Human-Computer Interaction Institute,Carnegie Mellon University
[2] Neowiz Internet Inc
[3] Department of Computer Science and Engineering,Hanyang University
关键词
activity recognition; activity theory; context-awareness; RFID;
D O I
暂无
中图分类号
TP391.44 [];
学科分类号
0811 ; 081101 ; 081104 ; 1405 ;
摘要
Activity recognition is a core aspect of ubiquitous computing applications.In order to deploy activity recognition systems in the real world,we need simple sensing systems with lightweight computational modules to accurately analyze sensed data.In this paper,we propose a simple method to recognize human activities using simple object information involved in activities.We apply activity theory for representing complex human activities and propose a penalized naive Bayes classifier for performing activity recognition.Our results show that our method reduces computation up to an order of magnitude in both learning and inference without penalizing accuracy,when compared to hidden Markov models and conditional random fields.
引用
收藏
页码:239 / 246
页数:8
相关论文
共 20 条
[1]  
Sensor information management mechanism for context-aware service in ubiquitous home. Baek Seung-Ho,Choi Eun-Chang,Huh Jae-Doo,et al. IEEE Transactions on Consumer Electronics . 2007
[2]  
Automatic video-based human motion analyzer for consumer surveillance system. Lao Weilun,Han Jungong,de With Peter H.N. IEEE Transactions on Consumer Electronics . 2009
[3]  
"Fine-grained activity recognition by aggregating abstract object usage,". D.J.Patterson,H.Kautz,M.Philipose. Proc.of the IEEE9th Int.Symposium on Wearable Computers . 2005
[4]  
Introduction to Statistical Relational Learning. Sutton C,McCallum A. . 2006
[5]  
Improving the recognition of interleaved activities. Modayil J,Bai T,Kautz H. Proc. the 10th Int. Conf. Ubiquitous Computing . 2008
[6]  
Sensing from the basement: A feasibility study of unobtrusive and low-cost home activity recognition. Fogarty J,Au C,Hudson S E. Proc. the 19th ACM Symp. User Interface Software and Technology . 2006
[7]  
Relational transformation-based tagging for human activity recognition. Landwehr N,Gutmann B,Thon I,Philipose M,Raedt L D. Proc. the 6th Int. Workshop on Multi-Relational Data Mining . 2007
[8]  
Accurate activity recognition in a home setting. Kasteren T V,Noulas A,Englebienne G,Krose B. Proc. the 10th Int. Conf. Ubiquitous Computing . 2008
[9]  
Inferring high-level behavior from low-level sensors. Patterson D J,Fox D,Kautz H A. Proc. the 5th Int. Conf. Ubiquitous Computing . 2003
[10]  
UMONS: Ubiquitous monitoring system in smart space. Lee H,Lim S,Kim J. IEEE Transactions on Consumer Electronics . 2009