Elderly Fall Detection With Vital Signs Monitoring Using CW Doppler Radar

Falling is the main cause of disability and fatality of elderly. In this work we used a 24 GHz continuous-wave Doppler radar to develop a low price and a highly accurate fall detection system aiming at observing indoor human activities and detecting fall accidents, thus limiting the consequences of undiscovered falls. A radar sensor was selected due to its capability of tracking human motions, passing through obstacles, and not being affected by light conditions. The sampled signals from the sensor were subjected to different feature engineering and machine learning techniques in order to determine the most characteristic features. Consequently, we were able to extract nontraditional radar features. Moreover, to improve the systems responsiveness and to lessen the hardware constraints, we added an additional pre-processing step to detect major physical activities with O(N) complexity. As part of fall detection, we also implemented vital signs monitoring to reduce false positives and to alert concerned authorities if necessary. Experimental results show that our proposed system has 90% recall rate for fall detection and 97.7% and 95.3% accuracy rates for respiration and heartbeat detection respectively.