A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks

Jun Ki Min, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Multiple sensor-based context inference systems can perceive users' tasks in detail while it requires complicated recognition models with larger resources. Such limitations make the systems difficult to be used for the mobile environment where the context-awareness would be most needed. In order to design and operate the complex models efficiently, this paper proposes an evolutionary process for generating the context models and a selective inference method. Dynamic Bayesian networks are employed as the context models to cope with the uncertain and noisy time-series sensor data, where the operations are managed by using the semantic network which describes the hierarchical and semantic relations of the contexts. The proposed method was validated on a wearable system with variable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings
Pages444-451
Number of pages8
Volume6678 LNAI
EditionPART 1
DOIs
Publication statusPublished - 2011 Jun 7
Event6th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2011 - Wroclaw, Poland
Duration: 2011 May 232011 May 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6678 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2011
CountryPoland
CityWroclaw
Period11/5/2311/5/25

Fingerprint

Dynamic Bayesian Networks
Evolutionary Optimization
Bayesian networks
Context-aware
Sensor
Sensors
Semantics
Semantic Network
Context-awareness
Gyroscope
Accelerometer
Gyroscopes
Accelerometers
Model
Time series
Resources
Context

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Min, J. K., & Cho, S. B. (2011). A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks. In Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings (PART 1 ed., Vol. 6678 LNAI, pp. 444-451). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6678 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-21219-2-56
Min, Jun Ki ; Cho, Sung Bae. / A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks. Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings. Vol. 6678 LNAI PART 1. ed. 2011. pp. 444-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
@inproceedings{439887a496264cb3bda54dd503b3d1b3,
title = "A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks",
abstract = "Multiple sensor-based context inference systems can perceive users' tasks in detail while it requires complicated recognition models with larger resources. Such limitations make the systems difficult to be used for the mobile environment where the context-awareness would be most needed. In order to design and operate the complex models efficiently, this paper proposes an evolutionary process for generating the context models and a selective inference method. Dynamic Bayesian networks are employed as the context models to cope with the uncertain and noisy time-series sensor data, where the operations are managed by using the semantic network which describes the hierarchical and semantic relations of the contexts. The proposed method was validated on a wearable system with variable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves.",
author = "Min, {Jun Ki} and Cho, {Sung Bae}",
year = "2011",
month = "6",
day = "7",
doi = "10.1007/978-3-642-21219-2-56",
language = "English",
isbn = "9783642212185",
volume = "6678 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "444--451",
booktitle = "Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings",
edition = "PART 1",

}

Min, JK & Cho, SB 2011, A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks. in Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings. PART 1 edn, vol. 6678 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6678 LNAI, pp. 444-451, 6th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2011, Wroclaw, Poland, 11/5/23. https://doi.org/10.1007/978-3-642-21219-2-56

A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks. / Min, Jun Ki; Cho, Sung Bae.

Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings. Vol. 6678 LNAI PART 1. ed. 2011. p. 444-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6678 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks

AU - Min, Jun Ki

AU - Cho, Sung Bae

PY - 2011/6/7

Y1 - 2011/6/7

N2 - Multiple sensor-based context inference systems can perceive users' tasks in detail while it requires complicated recognition models with larger resources. Such limitations make the systems difficult to be used for the mobile environment where the context-awareness would be most needed. In order to design and operate the complex models efficiently, this paper proposes an evolutionary process for generating the context models and a selective inference method. Dynamic Bayesian networks are employed as the context models to cope with the uncertain and noisy time-series sensor data, where the operations are managed by using the semantic network which describes the hierarchical and semantic relations of the contexts. The proposed method was validated on a wearable system with variable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves.

AB - Multiple sensor-based context inference systems can perceive users' tasks in detail while it requires complicated recognition models with larger resources. Such limitations make the systems difficult to be used for the mobile environment where the context-awareness would be most needed. In order to design and operate the complex models efficiently, this paper proposes an evolutionary process for generating the context models and a selective inference method. Dynamic Bayesian networks are employed as the context models to cope with the uncertain and noisy time-series sensor data, where the operations are managed by using the semantic network which describes the hierarchical and semantic relations of the contexts. The proposed method was validated on a wearable system with variable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves.

UR - http://www.scopus.com/inward/record.url?scp=79957895592&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79957895592&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-21219-2-56

DO - 10.1007/978-3-642-21219-2-56

M3 - Conference contribution

AN - SCOPUS:79957895592

SN - 9783642212185

VL - 6678 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 444

EP - 451

BT - Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings

ER -

Min JK, Cho SB. A hybrid context-aware wearable system with evolutionary optimization and selective inference of dynamic Bayesian networks. In Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings. PART 1 ed. Vol. 6678 LNAI. 2011. p. 444-451. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-21219-2-56