Paper Info
Title
A Bayesian Network Based Method For Activity Prediction In A Smart Home System
Abstract
A smart home system can provide better services to assist users if it knows what user activities will occur beforehand. Early research in activity prediction has indicated that the result of prediction is unique, but the accuracy remains unsatisfactory if only one result is considered. To solve this problem, this paper proposes a method of leveraging multiple models. In this work, we use a Bayesian network to build a model to predict which activity will happen, and then the predicted results go through a property filtering to get the final result. Due to the possibility that residents in a smart home may have different activity patterns, we have built a Bayesian network model to learn conditional probability of resident activities from the dataset of CASAS project. At last, we compare the results and show that our method has improved coverage and accuracy in activity prediction. This proposed method belongs to an ongoing project involving learning and control in a smart home system.
Year
Venue
Keywords
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Bayesian network, activity prediction, activity forecasting
Field
DocType
ISSN
Data mining,Data modeling,Conditional probability,Computer science,Filter (signal processing),Home automation,Prediction algorithms,Bayesian network,Artificial intelligence,Cybernetics,Machine learning,Multiple Models
Conference
1062-922X
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Zong-Hong Wu100.34
Alan Liu214917.19
Pei-Chuan Zhou310.68
Yen Feng Su400.34