Title: Artificial intelligence for stress monitoring and prediction using wearable sensors in internet of things

Authors: Liejiang Huang; Sichao Chen; Dilong Shen; Yuanchao Hu; Yuanjun Pan; Ligang Pan

Addresses: Hangzhou Xinmei Complete Electric Appliance Manufacturing Co., Ltd., Hangzhou 310000, China ' Hangzhou Xinmei Complete Electric Appliance Manufacturing Co., Ltd., Hangzhou 310000, China ' Hangzhou Xinmei Complete Electric Appliance Manufacturing Co., Ltd., Hangzhou 310000, China ' School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China ' Hangzhou Xinmei Complete Electric Appliance Manufacturing Co., Ltd., Hangzhou 310000, China ' Hangzhou Xinmei Complete Electric Appliance Manufacturing Co., Ltd., Hangzhou 310000, China

Abstract: The internet of medical things (IoMT) is considered as a middle platform between medical systems and communication systems. Machine learning (ML) is used as a new innovation prediction method for supporting stress monitoring factors in safety-critical information in IoMT environments. Therefore, ML approaches can support safety, accuracy and security for sensitive personal information in medical systems and healthcare applications. This paper presents a new literature management for main concept of ML methodologies on the IoMT ecosystem. Hence, the optimality of Stress monitoring is the primary research area in wearable sensors. Several contemporary papers exist on this important subject. The research gap in reviewing all these ML algorithms has motivated us for their presentation in the form of a detailed technical analysis in this paper. Architectures of IoMT are introduced prior to the development of the factors governing the stress monitoring decision making process and their reviews.

Keywords: machine learning; stress monitoring; wearable sensors; internet of medical things; IoMT.

DOI: 10.1504/IJBET.2023.131700

International Journal of Biomedical Engineering and Technology, 2023 Vol.42 No.1, pp.34 - 51

Received: 31 Aug 2022
Accepted: 20 Oct 2022
Published online: 27 Jun 2023 *

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