Stress monitoring using wearable sensors: IoT techniques in medical field

Abstract

The concept “Internet of Things” (IoT), which facilitates communication between linked devices, is relatively new. It refers to the next generation of the Internet. IoT supports healthcare and is essential to numerous applications for tracking medical services. By examining the pattern of observed parameters, the type of the disease can be anticipated. For people with a range of diseases, health professionals and technicians have developed an excellent system that employs commonly utilized techniques like wearable technology, wireless channels, and other remote equipment to give low-cost healthcare monitoring. Whether put in living areas or worn on the body, network-related sensors gather detailed data to evaluate the patient's physical and mental health. The main objective of this study is to examine the current e-health monitoring system using integrated systems. Automatically providing patients with a prescription based on their status is the main goal of the e-health monitoring system. The doctor can keep an eye on the patient's health without having to communicate with them. The purpose of the study is to examine how IoT technologies are applied in the medical industry and how they help to raise the bar of healthcare delivered by healthcare institutions. The study will also include the uses of IoT in the medical area, the degree to which it is used to enhance conventional practices in various health fields, and the degree to which IoT may raise the standard of healthcare services. The main contributions in this paper are as follows: (1) importing signals from wearable devices, extracting signals from non-signals, performing peak enhancement; (2) processing and analyzing the incoming signals; (3) proposing a new stress monitoring algorithm (SMA) using wearable sensors; (4) comparing between various ML algorithms; (5) the proposed stress monitoring algorithm (SMA) is composed of four main phases: (a) data acquisition phase, (b) data and signal processing phase, (c) prediction phase, and (d) model performance evaluation phase; and (6) grid search is used to find the optimal values for hyperparameters of SVM (C and gamma). From the findings, it is shown that random forest is best suited for this classification, with decision tree and XGBoost following closely behind.

Infection prevention and early warning in neonatal intensive care unit based on physiological sensor monitoring

“…Physiological sensors are an important perception and component of the Internet of Things, which is also a sensing device used in healthcare, disease monitoring, and prevention ( Diwakar et al, 2022 ). The working principle of physiological sensors is to be placed or implanted inside the human body to obtain human physiological information data ( Talaat and El-Balka, 2023 ).…”

Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring

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“…Over the past few decades, wearable textile-based “Internet of Things” devices have been demonstrated to be powerful tools for connecting various medical equipment to built-in sensors. Use of IoT in healthcare enables low-cost monitoring solutions by leveraging existing technologies, such as smartphones and wearable devices [ 6 , 18 ], which allows healthcare professionals to deliver superior health services across remote rural areas [ 19 ]. Incorporation of IoT technologies to smart sleeves enable the development of sEMG devices with both analog and digital processing capabilities that can undertake real-time monitoring of the muscle activities.…”

A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals

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