On the use of FIS inside a Telehealth system for cardiovascular risk monitoring

Cardiovascular diseases are the first cause of death in Italy. This has been worsened by the COVID-19 pandemic we are living in. Indeed, worldwide citizens are invited to stay at home to reduce the spreading of the virus, in the hospitals the priority is given to patients affected by COVID-19, and often patients affected by other diseases prefer to postpone routine check-ups, thus aggravating their health condition. There is a need for continuous monitoring of patients at risk, while contacts should be avoided. Telehealth systems, together with smart objects, are able to create assisted environments where patients are remotely and continuously monitored by the medical staff. In this paper, we present the overall architecture of a telehealth system, where vital parameters related to cardiovascular diseases such as heart rate, respiration rate, blood oxygen saturation, and color of lips are collected through a contact-less smart object. Based on these parameters, the level of cardiovascular risk is predicted through a Fuzzy Inference System (FIS) which provides a highly interpretable model against a lower accuracy [1]. To investigate the extent to which the loss of accuracy can be balanced by the acquired interpretability, in this work, we compare the FIS model with black-box models derived by standard machine learning algorithms. Experiments show that the performance of the FIS model is comparable with those of black-box models. Moreover, the FIS is easy to implement and it is easily explainable, thus it is worth in the medical domain where either patients and medical staff need to understand and trust the prediction made by machines. © 2021 IEEE.

A mHealth solution for contact-less self-monitoring of blood oxygen saturation

Mobile health (mHealth) technologies play a fundamental role in epidemiological situations such as the ongoing outbreak of COVID-19 because they allow citizen to self-monitor their health status while staying at home and being constantly in remote connection with the physicians despite the quarantine. Special care should be given to self-monitoring vital parameters such as blood oxygen saturation (SpO2), whose abnormal values are a warning sign for potential infection by COVID-19. Measurement of SpO2 is commonly made through the pulse oximeter that requires skin contact and hence could be a potential way of spreading contagious infections. For this reason, contact-less solutions for self-monitoring of SpO2 would be beneficial. In this paper we present a mHealth approach to self-monitor SpO2 that does not require any contact device since it is based on video processing. Video frames of the patient's face acquired by a camera are processed in real-time in order to extract the remote photoplethysmography signal useful to derive an estimation of SpO2. Preliminary experimental results show that the SpO2 values obtained by our contact-less solution are consistent with the measurements of a commercial pulse oximeter used as reference device.