Stress monitoring using wearable sensors: IoT techniques in medical field

Neural Computing and Applications (2023) 35:18571–18584 https://doi.org/10.1007/s00521-023-08681-z (0123456789().,-volV)(0123456789().,-volV) ORIGINAL ARTICLE Stress monitoring using wearable sensors: IoT techniques in medical field Fatma M. Talaat1 • Rana Mohamed El-Balka2 Received: 7 February 2023 / Accepted: 10 May 2023 / Published online: 2 June 2023  The Author(s) 2023 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. Keywords Stress monitoring  Wearable sensors  Medical field  IoT 1 Introduction & Fatma M. Talaat Rana Mohamed El-Balka 1 Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt 2 Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt The Internet of things (IoT) has transformed the healthcare industry by enabling remote monitoring of patients’ health status [1]. The integration of wearable sensors with IoT technologies has enabled continuous monitoring of physiological signals, leading to early detection of health problems and the ability to intervene before the condition worsens. Stress is a common health problem that can lead to various physical and mental disorders. IoT has the potential to be utilized in numerous industries, including efficient energy, transportation, agriculture, university campus connectivity, healthcare, logistics, and others, 123 18572 allowing physical objects to communicate and share information with one another through the internet [1]. IoT technology and machine learning (ML) have significantly impacted healthcare by shifting routine medical tests and healthcare services from hospitals to homes, making it easier for patients and healthcare professionals to utilize medical equipment [2]. With IoT integration, portable sensors can provide more precise data, and medical devices’ usability can be enhanced by implementing an android program in conjunction with IoT. The implementation of various technologies, particularly IoT, is likely to have a significant impact on all sectors, particularly in the medical field, as it can improve people’s quality of life [2]. IoT and ML have various applications in healthcare and daily life, with the internet’s rapid growth decreasing the use of traditional patient service methods and increasing the use of electronic healthcare systems, providing patients and healthcare professionals with access to advanced medical equipment through IoT technology. ML and IoT devices are beneficial in various categories, such as remote monitoring and mechanical automation, offering convenience, cost savings, and increased patient satisfaction in medical care applications [3]. In the IoT healthcare system, a sensor can be identified as a ‘‘thing’’ based on three key characteristics. Firstly, it should be able to recognize and gather environmental data such as temperature, light, and precipitation, as well as monitor pulse-related functions like electrocardiogram, blood glucose levels, and blood oxygen levels. Secondly, it should be capable of transmitting data autonomously to a centralized controller, either dynamically or through another system. Lastly, it should be able to be inactive after the operation is completed, alerting medical professionals to take immediate action if necessary [1, 4]. Additionally, DNA origami has emerged as versatile nanomachines for transportation, sensing, and computing in two-dimensional patterns, and three-dimensional assembly [5–7]. Researchers have conducted innovative studies to improve healthcare systems by offering various analytical applications for managing data sources, including electronic health records (EHRs) and medical images [8]. While the development of apps and services in healthcare is tailored to user needs, it is clear that services are designed based on what the developer has to offer. Recently, various ML techniques have been employed such as convolutional neural networks [9, 10] in multiple applications across various fields, such as efficiently grading alcohol dependence, estimating accident severity in severe injuries, and identifying emotions in functional technologies [11, 12]. In conclusion, IoT and ML have brought about significant advancements in healthcare and daily life. With the integration of IoT, wearable sensors, and ML, healthcare 123 Neural Computing and Applications (2023) 35:18571–18584 professionals can obtain real-time data on patients’ health status, enabling early detection and intervention before the condition worsens. The convenience, cost savings, and increased patient satisfaction of IoT devices have attracted attention in medical care applications, and the use of integrated technologies has the (...truncated)