Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Shakhnaz AKHMEDOVA; Vladimir STANOVOV; Sophia VISHNEVSKAYA; Chiori MIYAJIMA; Yukihiro KAMIYA

doi:10.1587/transcom.2020HMI0001

IEICE TRANSACTIONS on Communications

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Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Shakhnaz AKHMEDOVA, Vladimir STANOVOV, Sophia VISHNEVSKAYA, Chiori MIYAJIMA, Yukihiro KAMIYA

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Summary :

This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multi-objective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.

Publication: IEICE TRANSACTIONS on Communications Vol.E104-B No.6 pp.571-579

Publication Date: 2021/06/01

Publicized: 2020/12/24

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2020HMI0001

Type of Manuscript: Special Section INVITED PAPER (Special Section on Information and Communication Technology for Medical, Healthcare and Welfare Applications in Conjunction with Main Topics of ISMICT 2020)

Category: Navigation, Guidance and Control Systems

Authors

Shakhnaz AKHMEDOVA
  Reshetnev Siberian State University of Science and Technology
Vladimir STANOVOV
  Reshetnev Siberian State University of Science and Technology
Sophia VISHNEVSKAYA
  Reshetnev Siberian State University of Science and Technology
Chiori MIYAJIMA
  Aichi Prefectural University
Yukihiro KAMIYA
  Aichi Prefectural University

Keyword

classification, fuzzy logic, neural networks, non-contact vital sensing

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Shakhnaz AKHMEDOVA, Vladimir STANOVOV, Sophia VISHNEVSKAYA, Chiori MIYAJIMA, Yukihiro KAMIYA, "Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing" in IEICE TRANSACTIONS on Communications, vol. E104-B, no. 6, pp. 571-579, June 2021, doi: 10.1587/transcom.2020HMI0001.
Abstract: This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multi-objective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020HMI0001/_p

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@ARTICLE{e104-b_6_571,
author={Shakhnaz AKHMEDOVA, Vladimir STANOVOV, Sophia VISHNEVSKAYA, Chiori MIYAJIMA, Yukihiro KAMIYA, },
journal={IEICE TRANSACTIONS on Communications},
title={Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing},
year={2021},
volume={E104-B},
number={6},
pages={571-579},
abstract={This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multi-objective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.},
keywords={},
doi={10.1587/transcom.2020HMI0001},
ISSN={1745-1345},
month={June},}

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TY - JOUR
TI - Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing
T2 - IEICE TRANSACTIONS on Communications
SP - 571
EP - 579
AU - Shakhnaz AKHMEDOVA
AU - Vladimir STANOVOV
AU - Sophia VISHNEVSKAYA
AU - Chiori MIYAJIMA
AU - Yukihiro KAMIYA
PY - 2021
DO - 10.1587/transcom.2020HMI0001
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2021
AB - This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multi-objective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.
ER -

IEICE TRANSACTIONS on Communications