@inproceedings{cffa2ac3d7174eef83b662bb1f980d5b,
title = "How to high-efficiently acquire activity pattern in smart environment",
abstract = "The application of Smart Environment plays an important role in the development of advanced science and technology and therefore more and more attention. And activity recognition is the basis of Smart Environment, which reflects the intelligence of Smart Environment. However, there are two difficult and important problems which limiting the popularization of Smart Environment existing: high costs and difficulties in obtaining activity pattern. In order to overcome these problems and obtain activity pattern more effectively and efficiently, a framework for activity pattern transfer is proposed in this paper. There are two parts of activity pattern transfer: (i) Trajectory transfer, establishing the relationship on trajectories of template environment and new environment. (ii) Trigger duration transfer, transferring the trigger duration from template environment to new environment. There are four core algorithms of activity recognition based on transfer learning after pretreatment: candidate path set generation algorithm (CTSG), similarity computing algorithm (SC), trajectory mapping algorithm (TM) and trigger duration transfer algorithm (TDT). A lot of experiments had been done in the end to verify the efficiency of activity pattern transfer in simulation environment. And the experiments present the methods good time consuming performance and effectiveness.",
keywords = "Activity pattern, Activity trajectory, Smart environment, Transfer learning, Trigger duration",
author = "Chengliang Wang and Fei Ma and Yunpeng Wang and Debraj De and Das, {Sajal K.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2016, 9th IEEE International Conference on Social Computing and Networking, SocialCom 2016 and 2016 IEEE International Conference on Sustainable Computing and Communications, SustainCom 2016 ; Conference date: 08-10-2016 Through 10-10-2016",
year = "2016",
month = oct,
day = "26",
doi = "10.1109/BDCloud-SocialCom-SustainCom.2016.79",
language = "English",
series = "Proceedings - 2016 IEEE International Conferences on Big Data and Cloud Computing, BDCloud 2016, Social Computing and Networking, SocialCom 2016 and Sustainable Computing and Communications, SustainCom 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "501--508",
editor = "Zhipeng Cai and Guangchun Luo and Liang Cheng and Rafal Angryk and Yingshu Li and Anu Bourgeois and Wenzhan Song and Xiaojun Cao and Bhaskar Krishnamachari",
booktitle = "Proceedings - 2016 IEEE International Conferences on Big Data and Cloud Computing, BDCloud 2016, Social Computing and Networking, SocialCom 2016 and Sustainable Computing and Communications, SustainCom 2016",
}