Abstract

Fall incidents represent a major public health problem among elderly people. This resulted in a significant increase of the number of investigated systems aiming at detecting falls promptly. In this respect, in this work, a biomedical radar system is proposed for remote real-time fall detection and indoor localization. The system, consisting of a sensor and a base station, combines radar and wireless communication techniques, and uses a data processing technique to distinguish between fall events and normal movements. The classification, based on a Least-Square Support Vector Machine (LS -SVM), combined with the sliding window principle allows to perform fall detection in real-time. Moreover, it is capable to localize the subjects when the fall incident has been detected. The in-vivo validation showed a high success rate in detecting fall events, with a maximum delay of 340 ms. Moreover, a maximum mean absolute errors (MAE) of 3.8 cm and a maximum root-mean-square error (RMSE) of 7.5 cm were reported in measuring the subject’s absolute distance.