Detecting falls and vital signs via radar sensing

A novel ultra-wideband radar sensor system for simultaneous detection of falls and vital signs is presented. The suggested system is able to deal with real-life conditions, such as lack of real-fall data for training, body movements, several people present, and privacy issues. Micro-Doppler features, extracted from time-frequency spectrograms, are used to classify human actions as related to normal or abnormal activities (falls). A deep learning framework is used to extract and classify such features, also taking into account the specific way the older adult performs activity-of-daily-living actions. Preliminary validation results are very encouraging, showing the effectiveness to achieve good detection performance in assisted living scenarios.