Abstract
This research analyses the deployment of a human-centric IoT gadget for remote impacted person monitoring, employing sophisticated technology to beautify healthcare operations. The suggested approach incorporates a community of sensors, together with temperature, stress, coronary heart charge, and oxygen sensors, strategically situated at the afflicted person's frame. These sensors capture actual-time physiological information, which is processed via a signal converter, delivered to character controllers, and consolidated within the cloud for complete analysis. Subsequently, machine studying styles, including artificial neural network (ANN), decision tree (DT), random forest (RF), and naive bayes (NB), are used to anticipate impacted person fitness outcomes based at the accumulated dataset. The analysis assesses each version's performance using a dataset of 3233 items, of which 70% are designated for learning and 30% for experimentation. Results suggest that the proposed ANN model achieves an outstanding accuracy of 97.5%, outperforming DT, RF, and NB. Decision tree and random forest comply closely with accuracies of 92.33% and 91.22%, correspondingly, while naive bayes demonstrates a superb accuracy of 86.5%. These outcomes underline the potential of sophisticated machine learning models, notably ANN, within the field of remote affected person monitoring, giving a transformational method to healthcare. The merger of human-centric layout ideas, IoT technologies, and device learning contributes to the continuous dialogue on improving affected person care, opening the way for extra proactive, customized, and successful healthcare treatments. This investigation suggests a leap forward in utilising generation to alter healthcare practices, highlighting the crucial significance of facts-driven decision-making in making sure best patient impacts.
TopIntroduction
In the evolving landscape of healthcare, the integration of innovative technologies has remarkable potential for improving impacted person care. The introduction of the Internet of Things (IoT) has brought in a new technology of potential, especially within the realm of far-flung patient monitoring (Adeosun & Asiri, 2020; Aggarwal, 2022). This study objectives to develop a system for monitoring the health of the patient from the remote location. Several research are done in developing a remote health monitoring of the patients (Bilal & Sajid, 2022). This is accomplished using the set of various sensor and wireless data communication and cloud storage (Rani et al., 2022). In the setting of far-flung patient monitoring, human-centric design becomes vital, guaranteeing that the age is user-friendly, less intrusive, and comprises the varied wants of sufferers. Research emphasizes the good influence of human-centric layout in increasing impacted person engagement and adherence to remote monitoring techniques (Pathak & Kulkarni, 2015).
The Internet of Things has seen exponential expansion, providing remarkable potential for redesigning healthcare services. In afar patient monitoring, IoT programs play a vital role through supporting the smooth collection and transfer of physiological facts (Raza & Sajid, 2022). Wearable devices geared up with sensors, together with temperature, pressure, heart price, and oxygen sensors, offer continuous surveillance of sufferers in actual-time. Research underlines the potential of IoT in increasing the efficiency and accuracy of healthcare statistics collecting, establishing the muse for our research of a sophisticated IoT-based affected person monitoring gadget (Shaik et al., 2023).
The Internet of Things (IoT) has experienced unprecedented growth, presenting substantial opportunities for the transformation of healthcare services. In the realm of remote patient monitoring, IoT applications play a pivotal role by facilitating the seamless collection and transmission of physiological data (Singh, 2020; Sharan, 2015). Wearable devices equipped with an array of sensors, including those for temperature, pressure, heart rate, and oxygen levels, contribute to continuous real-time surveillance of patients. This integration of IoT technology holds great promise for enhancing the efficiency and accuracy of healthcare data collection.
Research underscores the immense potential of IoT in advancing the capabilities of patient monitoring systems. The continuous, real-time monitoring enabled by wearable IoT devices allows for a more comprehensive and dynamic understanding of patients' health status. The ability to collect diverse physiological data points in real-time offers a nuanced and holistic view, contributing to a more accurate assessment of patients' well-being (Sellamuthu et al., 2023).
Our research focuses on the development of an advanced IoT-based patient monitoring system, leveraging the capabilities of wearable devices and IoT applications. By harnessing the potential of IoT, we aim to enhance the precision and efficiency of healthcare data collection, ultimately leading to improved patient care and outcomes. The integration of IoT in patient monitoring not only streamlines the process of data collection but also opens avenues for proactive healthcare interventions based on real-time insights.
Key Terms in this Chapter
Controller: In RPM systems, this is a hardware component that receives data from various sensors attached to a patient, performs preliminary data analysis, and acts as a local processing hub. This reduces the need for continuous high-bandwidth communication with the main system.
Signal Converter: A device that takes input from the sensors and converts it into a signal that can be read and understood by other devices. It standardizes the sensor output to ensure consistent data quality and format for further processing and analysis.
Sensors: Devices that detect events or changes in the environment, and then provide corresponding outputs. In the context of RPM, sensors like temperature, pressure, heart rate, and oxygen sensors are used to collect vital physiological data from patients.
Data Transmission: The process of transferring data from one point to another. In RPM, this involves sending data from individual controllers to a central controller and then to the cloud, ensuring that patient data is continuously updated and accessible.
Cloud Storage: Online data storage services that allow data to be stored in logical pools across disparate, ubiquitous servers. In RPM, cloud storage is used to hold vast amounts of patient data securely, enabling easy access for analysis and long-term trend assessment.
Machine Learning Models in Medicine: The application of machine learning algorithms in medical settings to aid in diagnosis, treatment planning, and understanding disease processes. These models help in tailoring treatment plans to individual patients, improving diagnostic accuracy and treatment outcomes in complex diseases like breast cancer.
Remote Patient Monitoring (RPM): A healthcare practice that uses technology to monitor patients outside of conventional clinical settings, such as in the home or in a remote area, which helps improve access to care and decreases healthcare delivery costs.