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.

Meeting Name

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 (2016: Oct. 8-10, Atlanta, GA)

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Cloud computing; Conformal mapping; Distributed computer systems; Pattern recognition; Sustainable development; Trajectories; Activity patterns; Activity recognition; Similarity computing; Simulation environment; Smart environment; Trajectory transfer; Transfer learning; Trigger duration; Big data; Activity trajectory

International Standard Book Number (ISBN)

978-1-5090-3936-4

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Oct 2016

Link to Full Text

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