article · Neural Computing and Applications
Connected devices and wearable sensors offer methods for tracking patient wellbeing remotely by collecting detailed health data from living spaces or body-worn equipment. This research evaluates the use of Internet of Things technologies in medical settings to raise standards of care, enabling practitioners to oversee patient health without direct interaction. The work introduces a four-phase stress monitoring algorithm comprising data acquisition, signal processing, prediction, and performance evaluation. Wearable signals are imported, separated from non-signals, and subjected to peak enhancement prior to analysis. Multiple machine learning algorithms were evaluated and compared, including support vector machines tuned with grid search optimisation. Among the tested classification approaches, random forest achieved the best performance for identifying stress states, with decision tree and XGBoost algorithms demonstrating the next highest suitability.
Wearable devices and automated monitoring systems allow healthcare providers to evaluate physical and mental conditions remotely. By detecting stress patterns through machine learning without requiring direct clinical consultations, such systems can support continuous assessment. This approach illustrates how digital tools can assist clinical practices in tracking patient health indicators and potentially reducing the burden on conventional healthcare facilities.
This work applies to remote healthcare systems and wearable wellness devices aimed at stress detection. The primary users are healthcare institutions, clinicians tracking patient status remotely, and developers of digital health platforms. Because the research focuses on algorithmic pipeline development and comparative machine learning testing using imported sensor signals, it represents early-stage applied research that would require clinical validation and integration into compliant software before reaching market deployment.
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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.
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DOI: 10.1007/s00521-023-08681-z
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