Telehealth systems and applications are extensively investigated nowadays to enhance the quality-of-care and, in particular, to detect emergency situations and to monitor the well-being of elderly people, allowing them to stay at home independently as long as possible. In this paper, an embedded telehealth system for continuous, automatic, and remote monitoring of real-time fall emergencies is presented and discussed. The system, consisting of a radar sensor and base station, represents a cost-effective and efficient healthcare solution. The implementation of the fall detection data processing technique, based on the least-square support vector machines, through a digital signal processor and the management of the communication between radar sensor and base station are detailed. Experimental tests, for a total of 65 mimicked fall incidents, recorded with 16 human subjects (14 men and two women) that have been monitored for 320 min, have been used to validate the proposed system under real circumstances. The subjects' weight is between 55 and 90 kg with heights between 1.65 and 1.82 m, while their age is between 25 and 39 years. The experimental results have shown a sensitivity to detect the fall events in real time of 100% without reporting false positives. The tests have been performed in an area where the radar's operation was not limited by practical situations, namely, signal power, coverage of the antennas, and presence of obstacles between the subject and the antennas.

Fall incidents and the sustained injuries represent the main causes of accidents for elderly people, and also the third cause of chronic disability. The rapid detection of a fall event can reduce the mortality risk, avoiding also the aggravation of injuries. In this paper an embedded healthcare system based on a microwave radar is presented. A Continuous Wave (CW) Doppler radar is used to detect the changes in speed of different persons experienced during daily activities, namely falling and normal/random movements. The resulted speed signals are then processed in real-time by a digital signal processor (DSP) in order to detect fall incidents. Experimental results, conducted on real human volunteers in a real room setting, have shown a success rate of 100% in detecting fall events. Moreover, no false positives have been reported.