Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological parameters. Bearing in mind all the parameters involved, we developed a user-friendly wrist-wearable device combined with a web-based application, to adequately address this need. The proposed compilation obtains the photoplethysmogram (PPG) from the subject’s wrist and simultaneously extracts, in real time, the physiological parameters of heart rate (HR), blood oxygen saturation (SpO₂) and respiratory rate (RR), based on algorithms embedded on the wearable device. The described process is conducted solely within the device, favoring the optimal use of the available resources. The aggregated data are transmitted via Wi-Fi to a cloud environment and stored in a database. A corresponding web-based application serves as a visualization and analytics tool, allowing the individuals to catch a glimpse of their physiological parameters on a screen and share their digital information with health professionals who can perform further processing and obtain valuable health information.

This paper compares three supervised machine learning algorithms for healthcare monitoring. The first step of the presented work is the collection of vital signs information data that are utilized for training a Fuzzy Inference System (FIS), an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Neural Network (NN). Then, the trained algorithms are used to predict the Health Status (HS) of patients. Extended comparison results are demonstrated which indicate that the classifiers could be utilized as a basis for HS assessment.

Respiratory rate (RR) is a key bio parameter used in a wide range of clinical settings. Albeit its diagnostic and prognostic value it is still mostly measured manually by counting breaths. Several algorithms have been proposed to estimate RR from the electrocardiogram (ECG) and photoplethysmogram (PPG). These RR algorithms pave the way for automated electronic and unobtrusive measurement of RR in both health care and fitness monitoring. The approach introduced in this work for estimating RR is based on single wavelength reflectance-type PPG through a custom low-power wrist wearable device.

Resilience is defined as the capacity of a system to cope with a hazardous event or disturbance, responding or reorganizing in ways that maintain its essential function, identity, and structure, while also maintaining the capacity for adaptation, learning and transformation. A resilient health system is one that is capable to anticipate, respond to, cope with, recover from and adapt to system-related shocks and stress, so as to bring sustained improvements in population health, despite the unstable circumstances. Nowadays, the emergency department (ED) of hospitals faces growing demands, rising acuteness and longer waiting times. An efficient, accurate and resilient triage system to improve the operation of the ED becomes crucial. In this work, a resilient system for automatic priority, sorting and monitoring of medical events -triage system- is developed, so that the patients in the ED are treated according to the severity of their condition and not by the order of attendance utilizing a Fuzzy Inference System (FIS) that aggregates, processes patients’ vital signs as well as determines their Health Status (HS), according to which the ED staff performs the appropriate classification.