Evidential Network-Based Multimodal Fusion for Fall Detection
Paulo Armando Cavalcante Aguilar, Jérôme Boudy, Dan Istrate, Hamid Medjahed, Bernadette Dorizzi, João Mota, Jean-Louis Baldinger, Toufik Guettari, Imad Belfeki · International Journal of E-Health and Medical Communications · 2013
The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and Evidence theories such as Dempster-Shafer Theory (DST) are commonly used to handle the degree of uncertainty in the fusion processes. Based on a graphic representation of the DST called Evidential Networks, we propose a structure of heterogeneous multi-sensor fusion for falls detection. The proposed Evidential Network (EN) can handle the uncertainty present in a mobile and a fixed sensor-based remote monitoring systems (fall detection) by fusing them and therefore increasing the fall detection sensitivity compared to the a separated system alone.