Beschreibung
Activity clustering and recognition is an important research trend about smart home. Result clusters obtained from training process are applied in recognizing, controlling as well as forecasting activities inside a sensor smart home. Activities differ from each other at typical characteristics such as sensor sets triggered or temporal ones. In this work, we present a smart home infrastructure and propose an unsupervised approach of clustering in-home activities based on temporal characteristics. Experiment results show that the new approach creates more flexible and accurate clustering results when compared to the smart home system named AALO reported in the research work of Enamul Hoque et al. Besides, in this work it is proven that clustering results affects significantly to the outcome of next-activity forecasting process via experimental comparison results.
Autorenporträt
Duy Nguyen was born and raised in Binh Duong Province, Vietnam. Having got Bachelor degree in IT at the University of Technical Education in 2006. Graduated as Master of Information Technology at UIT, the National University of Vietnam. He currently works as a lecturer at the Faculty of Information Technology, Binh Duong University, Vietnam.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
