A multi-strategy Bayesian model for sensor fusion in smart environments

Muhammad Fahim; Muhammad Hameed Siddiqi; Sungyoung Lee; Young Koo Lee

doi:10.1109/ICCIT.2010.5711028

A multi-strategy Bayesian model for sensor fusion in smart environments

Muhammad Fahim^*, Muhammad Hameed Siddiqi, Sungyoung Lee, Young Koo Lee

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Sensor fusion became a powerful scheme to recognize the daily life activities in smart homes. This paper proposed a multi-strategy approach to overcome the challenges of accuracy and efficiency. We design a model to integrate It-Nearest Neighbor (k-NN, k=5) technique and Bayes classifier for recognizing the activities of daily living. There are three stages of this model. The first stage is used to reduce the search space by discovering the useful regions. A Bayes classifier is utilized in the second stage to refine the degree of beliefs. The confidence values have been denoted by the output of the Bayes classifier. Finally, max rule has been applied to fuse confidence values. The proposed model has been evaluated on five different types of activities from Place Lab dataset (PLIA1). We compare our Multi-strategy approach with the Naive Bayes Classifier and get 9% higher accuracy and 186 ms faster execution time.

Original language	English
Title of host publication	Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010
Pages	52-57
Number of pages	6
DOIs	https://doi.org/10.1109/ICCIT.2010.5711028
Publication status	Published - 2010
Externally published	Yes
Event	5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010 - Seoul, Korea, Republic of Duration: 30 Nov 2010 → 02 Dec 2010

Publication series

Name	Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010

Conference

Conference	5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010
Country/Territory	Korea, Republic of
City	Seoul
Period	30/11/2010 → 02/12/2010

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

Keywords

Bayesian classifier
Component
k-Nearest Neighbor (kNN)
Sensor fusion

ASJC Scopus subject areas

Information Systems
Computer Science (miscellaneous)

Access to Document

10.1109/ICCIT.2010.5711028

Cite this

Fahim, M., Siddiqi, M. H., Lee, S., & Lee, Y. K. (2010). A multi-strategy Bayesian model for sensor fusion in smart environments. In Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010 (pp. 52-57). [5711028] (Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010). https://doi.org/10.1109/ICCIT.2010.5711028

Fahim, Muhammad ; Siddiqi, Muhammad Hameed ; Lee, Sungyoung ; Lee, Young Koo. / A multi-strategy Bayesian model for sensor fusion in smart environments. Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010. 2010. pp. 52-57 (Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010).

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abstract = "Sensor fusion became a powerful scheme to recognize the daily life activities in smart homes. This paper proposed a multi-strategy approach to overcome the challenges of accuracy and efficiency. We design a model to integrate It-Nearest Neighbor (k-NN, k=5) technique and Bayes classifier for recognizing the activities of daily living. There are three stages of this model. The first stage is used to reduce the search space by discovering the useful regions. A Bayes classifier is utilized in the second stage to refine the degree of beliefs. The confidence values have been denoted by the output of the Bayes classifier. Finally, max rule has been applied to fuse confidence values. The proposed model has been evaluated on five different types of activities from Place Lab dataset (PLIA1). We compare our Multi-strategy approach with the Naive Bayes Classifier and get 9% higher accuracy and 186 ms faster execution time.",

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doi = "10.1109/ICCIT.2010.5711028",

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Fahim, M, Siddiqi, MH, Lee, S & Lee, YK 2010, A multi-strategy Bayesian model for sensor fusion in smart environments. in Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010., 5711028, Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010, pp. 52-57, 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010, Seoul, Korea, Republic of, 30/11/2010. https://doi.org/10.1109/ICCIT.2010.5711028

A multi-strategy Bayesian model for sensor fusion in smart environments. / Fahim, Muhammad; Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young Koo.

Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010. 2010. p. 52-57 5711028 (Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Siddiqi, Muhammad Hameed

AU - Lee, Sungyoung

AU - Lee, Young Koo

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AB - Sensor fusion became a powerful scheme to recognize the daily life activities in smart homes. This paper proposed a multi-strategy approach to overcome the challenges of accuracy and efficiency. We design a model to integrate It-Nearest Neighbor (k-NN, k=5) technique and Bayes classifier for recognizing the activities of daily living. There are three stages of this model. The first stage is used to reduce the search space by discovering the useful regions. A Bayes classifier is utilized in the second stage to refine the degree of beliefs. The confidence values have been denoted by the output of the Bayes classifier. Finally, max rule has been applied to fuse confidence values. The proposed model has been evaluated on five different types of activities from Place Lab dataset (PLIA1). We compare our Multi-strategy approach with the Naive Bayes Classifier and get 9% higher accuracy and 186 ms faster execution time.

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Fahim M, Siddiqi MH, Lee S, Lee YK. A multi-strategy Bayesian model for sensor fusion in smart environments. In Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010. 2010. p. 52-57. 5711028. (Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010). https://doi.org/10.1109/ICCIT.2010.5711028

A multi-strategy Bayesian model for sensor fusion in smart environments

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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