Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices

In order to improve the accuracy of the evaluation results of multiperception intelligent wearable devices, the mathematical statistical characteristics based on speech, behavior, environment, and physical signs are proposed; first, the PCA feature compression algorithm was used to reduce the dimens...

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Autores principales:	Xichao Dai, Yumei Ding
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/c65da924f80641d68007e85e5032ad20
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spelling	oai:doaj.org-article:c65da924f80641d68007e85e5032ad202021-11-29T00:56:33ZMental Health Monitoring Based on Multiperception Intelligent Wearable Devices1555-431710.1155/2021/8307576https://doaj.org/article/c65da924f80641d68007e85e5032ad202021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8307576https://doaj.org/toc/1555-4317In order to improve the accuracy of the evaluation results of multiperception intelligent wearable devices, the mathematical statistical characteristics based on speech, behavior, environment, and physical signs are proposed; first, the PCA feature compression algorithm was used to reduce the dimension of these features, and the differences among different training samples were compared and analyzed; then, three weak classifiers are designed using the logistic regression algorithm, and finally, a strong classifier with higher prediction accuracy is designed according to the boosting decision fusion method and ensemble learning idea. The results showed that the accuracy of the logistic regression model trained with the feature data of voice PCA was 0.964, but the recall rate and crossover results were significantly reduced to 0.844 and 0.846, respectively. The accuracy, accuracy and recall of the decision fusion model based on the boosting method and integrated learning are 0.969, and the prediction accuracy of K-folds cross-validation is also as high as 0.956; the superposition fusion results of three weak classifiers achieve a better classification effect.Xichao DaiYumei DingHindawi-WileyarticleMedical technologyR855-855.5ENContrast Media & Molecular Imaging, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medical technology R855-855.5
spellingShingle	Medical technology R855-855.5 Xichao Dai Yumei Ding Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices
description	In order to improve the accuracy of the evaluation results of multiperception intelligent wearable devices, the mathematical statistical characteristics based on speech, behavior, environment, and physical signs are proposed; first, the PCA feature compression algorithm was used to reduce the dimension of these features, and the differences among different training samples were compared and analyzed; then, three weak classifiers are designed using the logistic regression algorithm, and finally, a strong classifier with higher prediction accuracy is designed according to the boosting decision fusion method and ensemble learning idea. The results showed that the accuracy of the logistic regression model trained with the feature data of voice PCA was 0.964, but the recall rate and crossover results were significantly reduced to 0.844 and 0.846, respectively. The accuracy, accuracy and recall of the decision fusion model based on the boosting method and integrated learning are 0.969, and the prediction accuracy of K-folds cross-validation is also as high as 0.956; the superposition fusion results of three weak classifiers achieve a better classification effect.
format	article
author	Xichao Dai Yumei Ding
author_facet	Xichao Dai Yumei Ding
author_sort	Xichao Dai
title	Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices
title_short	Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices
title_full	Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices
title_fullStr	Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices
title_full_unstemmed	Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices
title_sort	mental health monitoring based on multiperception intelligent wearable devices
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/c65da924f80641d68007e85e5032ad20
work_keys_str_mv	AT xichaodai mentalhealthmonitoringbasedonmultiperceptionintelligentwearabledevices AT yumeiding mentalhealthmonitoringbasedonmultiperceptionintelligentwearabledevices
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Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices

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