Multimodal information fusion for automated recognition of complex agitation behaviors of dementia patients

This paper presents a new approach using multimodal information fusion for automated recognition of complex agitation behavior among persons with dementia. In particular, we present a hierarchical information fusion framework to model complex agitation behaviors based on a clinical agitation scale widely used in hospitals. We present the detailed features extraction and selection to represent low level atomic agitation behaviors, classifiers for atomic agitation behavior recognition and inference combiners to detect and rate the onset of complex agitation behaviors in dementia patients using multimodal sensors such as pressure sensors, ultrasound sensors, video cameras, acoustic sensors, etc., in a hospital ward.