Wideband radar based fall motion detection for a generic elderly

Radar-based automated fall detection systems are considered as an important and emerging technology for elderly assisted living. These radar systems provide non-intrusive sensing capabilities to detect fall events. Various studies have used micro-Doppler signatures to determine falls. However, Doppler radar fall detection systems suffer false alarms stemming from other sudden non-rhythmic motion articulations. In this work, we consider a textural-based feature extraction method which can determine the density variations between various motion articulations. For this purpose, textural features are extracted from the gray level co-occurrence matrix for each motion using time-integrated range-Doppler maps and micro-Doppler signatures. Textural features are then used to train the support vector machine classifier. The sequential forward selection method is implemented to identify essential features and minimize the feature space while maximizing the fall detection rate. The results show that well selected range-Doppler based textural features can provide improved classification results compared to textural features based only on micro-Doppler signatures.