Paper Info
Title
Adaptive Modeling of a User's Daily Life with a Wearable Sensor Network
Abstract
In an environment where the contexts of users are complex and the degree of freedom of user activity is very high, such as in daily life, several factors need to be considered for constructing user models. Such a model should include changes in the meanings of activities that reflect the user's situation both temporally and individually. In this paper we propose a novel approach for personalizing the user model and adapting it to individual circumstances with a wearable sensor network. We also describe the process for determining the repetitive activities of a user by using incremental clustering and Bayesian network. We show experimental results for an adaptive user model based on a real wearable sensor platform. Multimedia data of user experience are acquired from the multimodal sensors, and processed to metadata that have meanings.
Year
DOI
Venue
2008
10.1109/ISM.2008.56
ISM
Keywords
Field
DocType
real wearable sensor platform,bayesian network,multimodal sensor,adaptive modeling,wearable sensor network,daily life,adaptive user model,user experience,user activity,user model,wireless sensor networks,data mining,wearable computing,entropy,wearable computers,data models,meta data,computational modeling,context modeling,bayesian methods,sensor network,user modeling,adaptive system,wearable computer,learning artificial intelligence,degree of freedom
Data modeling,User experience design,Computer science,Context model,Human–computer interaction,Artificial intelligence,User modeling,Computer vision,Wearable computer,User interface design,User interface,Wireless sensor network,Machine learning
Conference
Citations 
PageRank 
References 
4
0.54
10
Authors
4
Name
Order
Citations
PageRank
Hyoungnyoun Kim193.39
Ig-Jae Kim239035.40
Hyoung-Gon Kim316420.34
Ji-Hyung Park411318.38