Model-Based and Class-Based Fusion of Multisensor Data

In the recent years, the advancement of technology, the constantly aging population and the developments in medicine have resulted in the creation of numerous ambient assisted living systems. Most of these systems consist of a variety of sensors that provide information about the health condition of patients, their activities and also create alerts in case of harmful events. Successfully combining and utilizing all the multimodal information is an important research topic. The current paper compares model-based and class-based fusion, in order to recognize activities by combining data from multiple sensors or sensors of different body placements. More specifically, we tested the performance of three fusion methods; weighted accuracy, averaging and a recently introduced detection rate based fusion method. Weighted accuracy and the detection rate based fusion achieved the best performance in most of the experiments.