Wearable systems and embedded sensors for physiological monitoring have been gaining large interest over the last decade in research and commercial fields. The key benefits introduced by these systems include their small size, lightness, low-power consumption and wearability. Major applications of such systems are related to medicine and healthcare allowing for out-patient care and enhancing the quality of life for chronic disease patients, and preventing unnecessary hospitalizations.
Prof. De Rossi and Prof. Scilingo have been extensively working at the University of Pisa (UNIPI) on the development of a comfortable sensorized t-shirt having dry textile-based electrodes able to monitor the electrocardiogram (ECG). The system is currently able to monitor ECG in real-time and store it in a portable memory.
However, the algorithms implemented in the t-shirt embedded electronics allow for the real-time monitoring of the mean heart rate only. Further analyses, in fact, have to be performed in an ad-hoc offline post-processing session.
Prof. Brown and Dr. Barbieri have been working at MIT on effective mathematical models, namely the point-process models, able to provide real-time monitoring of several ECG-derived measures that may have clinical relevance. Although the implementation of these point-process models is computationally efficient, these algorithms have never been implemented in a portable device.
This project aims at combining the efforts of these two groups from MIT and UNIPI in order to implement the point-process framework in textile-based wearable systems, thus identifying a novel personalized, pervasive, cost-effective, and multi-parametric system for the real-time long-term and short-term monitoring of cardiovascular dynamics.