CS-based fall detection for connected health applications

Fall-related injuries of elderly people have become a major public-health burden resulting in direct physical, physiological and financial costs to the surfer and indirect societal costs. Automated fall detectors play a central role in reducing these damages and in supporting safety and independency of the seniors. Typically, automated fall detection devices can send real time notifications to the caregivers in case of emergency. In this study, we consider the problem of fall detection of compressively sensed data. The proposed approach involves first, acquiring acceleration data from different subjects using different fall and activities of daily living (ADLs) scenarios by means of shimmer devices. The collected data is then, multiplied by a binary sensing matrix. Two classification approaches were investigated using k-nearest neighbour (KNN) and extended nearest neighbour (ENN), respectively. Our experiments showed promising results with accuracies of up to 91.34 % and 91.73 % on the test set using five and ten folds cross validation respectively.