Title: A profile based data segmentation for in-home activity recognition

Authors: Rania Al Nadi; Mohammed G.H. Al Zamil

Addresses: Department of Computer Information Systems, Yarmouk University, Defaa Street, 21163, Irbed, Jordan ' Department of Computer Information Systems, Yarmouk University, Defaa Street, 21163, Irbed, Jordan

Abstract: A major problem in smart-home activity recognition is the ambiguity of interpreting the actions that formulate activities. It resulted from the redundancy of irrelevant actions and the concurrent interleaving among activities themselves. In this paper, we present a framework to minimise the effect of such ambiguity using profile based data segmentation and actions refinement. The proposed methodology relies on defining a profile for each sensor in the environment for enriching existing features with semantic ones. Furthermore, according to these profiles, irrelevant actions within data segments are removed. Moreover, the proposed methodology addresses the interleaving among different activities. Experiments have been conducted to measure the performance of the proposed framework on different datasets. We evaluated our methodology using three different classifiers. The results indicated that the proposed framework achieved statistically significant improvements. Such enhancements resulted from minimising the effect of irrelevant actions and resolving the concurrent interleaving among activities within datasets.

Keywords: IoT; internet of things; smart home; activity recognition; data segmentation; data mining; sensor profile.

DOI: 10.1504/IJSNET.2019.097553

International Journal of Sensor Networks, 2019 Vol.29 No.1, pp.28 - 37

Available online: 25 Jan 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article