Statistical analysis with missing data
Statistical analysis with missing data
Communications of the ACM
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Gait-Based Recognition of Humans Using Continuous HMMs
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
User identification using user’s walking pattern over the ubiFloorII
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Workshop overview for the international workshop on situation, activity and goal awareness
Proceedings of the 13th international conference on Ubiquitous computing
| Hi-index | 0.00 |
This paper addresses the inhabitant prediction issue in smart houses based on daily life activities. We use data provided by non intrusive sensors and devices to predict the house occupant. Support Vector Machines (SVM) classifier was applied to build a Behavior Classification Model (BCM) and learn the users' habits when they perform activities for predicting and identifying the house occupant. The model was tested using data coming from the Washington State University smart apartment tesbed and data from experiment held with six users at the DOMUS apartment. The BCM model results was also compared with a frequency based approach.