Ranking risk exposures for situational surveillance of falls with sensors

Falls pose a significant public health problem. The usefulness of wearable sensors for detection of falls is well-known. We propose a novel optimization-based approach that was formulated for ranking exposures to falls using information streamed from multiple sensors. Our method incorporates statistical estimation. We illustrate our technique on synthetic data. Future research is aimed at assessing the validity of this new method.