Machine Learning and IoT for Stress Detection and Monitoring

被引：1

作者：

Hadhri, Sami ^{[1
]}

Hadiji, Mondher ^{[1
]}

Labidi, Walid ^{[2
]}

机构：

[1] Higher Inst Technol Studies Sfax, Sfax, Tunisia

[2] Fac Sci Sfax, Sfax, Tunisia

来源：

ADVANCES IN COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2022 | 2022年 / 1653卷

关键词：

IoT; Stress monitoring; Pulse oximetry; Heart rate; Machine Learning;

D O I：

10.1007/978-3-031-16210-7_44

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a Machine Learning and IoT based system for the detection and monitoring of patient stress. This system consists of a medical kit that uses sensors placed on top of the patient's hand to measure oxygen saturation, heart rate, and galvanic skin response before sending the data to the Firebase server. Five Machine Learning algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest) were implemented using holdout and K-fold cross-validation on a Raspberry board installed in the doctor's office. Our system can make predictions with the Random Forest classifier with a value that reaches 87%.

引用

页码：542 / 553

页数：12

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