Real-time data analysis in health monitoring systems: A comprehensive systematic literature review.

Health monitoring systems (HMSs) capture physiological measurements through biosensors (sensing), obtain significant properties and measures from the output signal (perceiving), use algorithms for data analysis (reasoning), and trigger warnings or alarms (acting) when an emergency occurs. These systems have the potential to enhance health care delivery in different application domains, showing promising benefits for health diagnosis, early symptom detection, disease prediction, among others. However, the implementation of HMS presents challenges for sensing, perceiving, reasoning, and acting based on monitored data, mainly when data processing should be performed in real time. Thus, the quality of these diagnoses relies heavily on the data and data analysis methods applied. Data mining techniques have been broadly investigated in health systems; however, it is not clear what real-time data analysis techniques are best suited for each context. This work carries out a search in five scientific electronic databases to identify recent studies that investigated HMS using real-time data analysis techniques. Thirty-six research studies were selected after screening 2,822 works. Applied data analysis methods, application domains, utilized sensors, physiological parameters, extracted features, claimed benefits, limitations, datasets used, and published results were described, compared and analyzed. The findings indicate that machine learning methods are trending in such studies. There is no universal solution for all health domains; however, support vector machines are a predominant method. Among the application domains, cardiovascular disease is the most investigated. Most reviewed studies reported improvements in performing data mining tasks or operational modes of solutions. Although studies tested algorithms and presented promising results, those are particular for each experiment. This review gives a comprehensive overview of HMS real-time data analysis and points to directions for future research.

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home.

Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients' status. Internet of Things (IoT) technologies have been used for monitoring patients' health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.