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Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography

A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine...

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Detalles Bibliográficos
Autores principales: Wang, Hengyang, Lu, Dongcheng, Liu, Li, Gao, Han, Wu, Rumeng, Zhou, Yueling, Ai, Qing, Wang, You, Li, Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588107/
https://www.ncbi.nlm.nih.gov/pubmed/34770272
http://dx.doi.org/10.3390/s21216965
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author Wang, Hengyang
Lu, Dongcheng
Liu, Li
Gao, Han
Wu, Rumeng
Zhou, Yueling
Ai, Qing
Wang, You
Li, Guang
author_facet Wang, Hengyang
Lu, Dongcheng
Liu, Li
Gao, Han
Wu, Rumeng
Zhou, Yueling
Ai, Qing
Wang, You
Li, Guang
author_sort Wang, Hengyang
collection PubMed
description A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached R2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model’s performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.
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spelling pubmed-85881072021-11-13 Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography Wang, Hengyang Lu, Dongcheng Liu, Li Gao, Han Wu, Rumeng Zhou, Yueling Ai, Qing Wang, You Li, Guang Sensors (Basel) Article A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached R2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model’s performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG. MDPI 2021-10-20 /pmc/articles/PMC8588107/ /pubmed/34770272 http://dx.doi.org/10.3390/s21216965 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Hengyang
Lu, Dongcheng
Liu, Li
Gao, Han
Wu, Rumeng
Zhou, Yueling
Ai, Qing
Wang, You
Li, Guang
Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_full Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_fullStr Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_full_unstemmed Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_short Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography
title_sort quantitatively recognizing stimuli intensity of primary taste based on surface electromyography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588107/
https://www.ncbi.nlm.nih.gov/pubmed/34770272
http://dx.doi.org/10.3390/s21216965
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