A fuzzy logic approach for a wearable cardiovascular and aortic monitoring system

Abstract

A new methodology for fault detection on wearable medical devices is proposed. The basic strategy relies on correctly classifying the captured physiological signals, in order to identify whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the physiological signals, like the electrocardiogram (ECG) and blood pressure (BP), to increase the trust levels of the captured data after rejecting or correcting distorted vital signals from each sensor, and to provide additional information on the patient's condition by classifying the set of signals into normal or abnormal condition (e.g. arrhythmia, chest angina, and stroke). Once an abnormal situation is detected in one or several sensors the monitoring system runs a set of tests in a fast and energy efficient way to check if the wearer shows a degradation of his health condition or the system is reporting erroneous values.