Authors: Tao Xu; Yun Zhou; Bertrand David; René Chalon
Addresses: School of Software and Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an Shaanxi, 710072, China ' School of Education, Shaanxi Normal University, 199, South Chang'an Road, Xi'an Shaanxi, 710062, China ' Université de Lyon, CNRS, Ecole Centrale de Lyon, LIRIS, UMR5205, Ecully, 69134, France ' Université de Lyon, CNRS, Ecole Centrale de Lyon, LIRIS, UMR5205, Ecully, 69134, France
Abstract: Recently, 'activity' context draws increased attention from researchers in context awareness. Existing context-aware middleware usually employ the rule-based method to, which is easy to build and also intuitive to work with. However, this method is fragile, not flexible enough, and is inadequate to support diverse types of tasks. In this paper, we surveyed the related literature in premier conferences over the past decade, reviewed the main activity context recognition methods, and summarised their three main facets: basic activity inference, dynamic activity analysis, and future activity recommendation. Based on our previous work, we then proposed an intelligent inference engine for our context-aware middleware. Besides satisfying requirements for checking context consistency, our inference engine integrates the three methods for activity context recognition to provide a solution for all facets of activity context recognition based on our context-aware middleware.
Keywords: context awareness; context reasoning; activity recognition; intelligent inference engine; context-aware middleware; sensor networks; context consistency; activity context recognition.
International Journal of Sensor Networks, 2016 Vol.22 No.3, pp.145 - 157
Available online: 04 Nov 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article