A multi - Modal decision making system for an ambient assisted living environment

Modern ubiquitous services demand the fusion of data from multiple modalities, but so far few approaches have achieved to present such solutions. This challenge becomes even more demanding when the AAL environment comes to serve the requirements of elderly people who need continuous monitoring and care. In light of this, this paper presents a multi - modal decision making system that consists of mixed knowledge and nonknowledge based subsystems that deliver the appropriate intelligence among three modalities (i.e. ambient, health, fall detection). The main effort has been given on the health status assessment module, where a SVM classifier has been trained using the Physionet's MIT-BIH Arrhythmia database in order to detect abnormal heart beats based on time-domain and statistical features. An initial study on the classification scheme showed satisfactory results for the purposes of a system that is responsible of early screening and dangerous event detection.