A non invasive, wearable sensor platform for multi-parametric remote monitoring in CHF patients

Abstract

There is an increasing need to find new ways of managing the European healthcare models due to the demographic and socio-economic challenges that result from the fast ageing of the population. In particular, the increasing number of elderly people directly entails an increasing number of patients with cardiovascular diseases and, in particular, with Congestive Heart Failure (CHF) issues. Although with limited physical activity, this type of patients usually remains at home, outside the hospital environment. However, this disease causes that their health status continues to worsen with episodes of crisis leading to acute deterioration. These episodes, which require emergency and long-time hospital admissions, are always preceded by noticeable changes in several physiological parameters. In this context, accurate and reliable remote monitoring solutions based on state-of-the-art technologies take a main role in order to predict the deterioration of CHF patients and improve their quality of life. In the present paper a prototype of an implemented non-invasive, wearable sensor platform for Congestive Heart Failure (CHF) patients is shown and described. The platform monitors all the required parameters from sensors, collects and processes the data in a mobile platform and sends the data to a server. Specifically, the present solution monitors the electrocardiogram (ECG), potassium blood content (obtained from ECG), average energy expenditure evaluation through activity recognition, skin temperature and sweating. The energy expenditure for all the activities was estimated with a mean absolute error of 0.85 MET. The error on HR measurements was lower than the 10 %.