Comparison of situation awareness algorithms for remote health monitoring with smartphones

Abstract

Telemedicine applications provide healthcare services through communications technologies overcoming the geographical separation between patients and caregivers. These services can be provided via wireless devices, such as smart-phones with dedicated applications. An interesting application concerns the so-called situation awareness algorithms and, in particular, the Activity Recognition (AR) aimed at tracking the physical activity (or movements) of patients that need a constant monitoring of their medical conditions. This work takes as reference an architecture applicable, but not limited to, patients suffering from Heart Failure (HF) and presents a performance comparison between AR approaches based on the accelerometer signal captured through the patients' smartphones. In more detail, the considered AR techniques apply two different classifiers used to decide the patients movements: a J48 decision tree and a Support Vector Machine (SVM). For each classifier, three different features sets, characterizing the accelerometer signal, have been employed. The performance are evaluated both in terms of accuracy-related metrics and time needed by each classifiers to perform the decision. The results show that SVM provides the best accuracy while the J48 requires less classification time.