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A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals
Existing research on stress recognition focuses on the extraction of physiological features and uses a classifier that is based on global optimization. There are still challenges relating to the differences in individual physiological signals for stress recognition, including dispersed distribution...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359102/ https://www.ncbi.nlm.nih.gov/pubmed/30669646 http://dx.doi.org/10.3390/s19020429 |
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author | Li, Minjia Xie, Lun Wang, Zhiliang |
author_facet | Li, Minjia Xie, Lun Wang, Zhiliang |
author_sort | Li, Minjia |
collection | PubMed |
description | Existing research on stress recognition focuses on the extraction of physiological features and uses a classifier that is based on global optimization. There are still challenges relating to the differences in individual physiological signals for stress recognition, including dispersed distribution and sample imbalance. In this work, we proposed a framework for real-time stress recognition using peripheral physiological signals, which aimed to reduce the errors caused by individual differences and to improve the regressive performance of stress recognition. The proposed framework was presented as a transductive model based on transductive learning, which considered local learning as a virtue of the neighborhood knowledge of training examples. The degree of dispersion of the continuous labels in the y space was also one of the influencing factors of the transductive model. For prediction, we selected the epsilon-support vector regression (e-SVR) to construct the transductive model. The non-linear real-time features were extracted using a combination of wavelet packet decomposition and bi-spectrum analysis. The performance of the proposed approach was evaluated using the DEAP dataset and Stroop training. The results indicated the effectiveness of the transductive model, which had a better prediction performance compared to traditional methods. Furthermore, the real-time interactive experiment was conducted in field studies to explore the usability of the proposed framework. |
format | Online Article Text |
id | pubmed-6359102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63591022019-02-06 A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals Li, Minjia Xie, Lun Wang, Zhiliang Sensors (Basel) Article Existing research on stress recognition focuses on the extraction of physiological features and uses a classifier that is based on global optimization. There are still challenges relating to the differences in individual physiological signals for stress recognition, including dispersed distribution and sample imbalance. In this work, we proposed a framework for real-time stress recognition using peripheral physiological signals, which aimed to reduce the errors caused by individual differences and to improve the regressive performance of stress recognition. The proposed framework was presented as a transductive model based on transductive learning, which considered local learning as a virtue of the neighborhood knowledge of training examples. The degree of dispersion of the continuous labels in the y space was also one of the influencing factors of the transductive model. For prediction, we selected the epsilon-support vector regression (e-SVR) to construct the transductive model. The non-linear real-time features were extracted using a combination of wavelet packet decomposition and bi-spectrum analysis. The performance of the proposed approach was evaluated using the DEAP dataset and Stroop training. The results indicated the effectiveness of the transductive model, which had a better prediction performance compared to traditional methods. Furthermore, the real-time interactive experiment was conducted in field studies to explore the usability of the proposed framework. MDPI 2019-01-21 /pmc/articles/PMC6359102/ /pubmed/30669646 http://dx.doi.org/10.3390/s19020429 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Minjia Xie, Lun Wang, Zhiliang A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals |
title | A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals |
title_full | A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals |
title_fullStr | A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals |
title_full_unstemmed | A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals |
title_short | A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals |
title_sort | transductive model-based stress recognition method using peripheral physiological signals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359102/ https://www.ncbi.nlm.nih.gov/pubmed/30669646 http://dx.doi.org/10.3390/s19020429 |
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