Advances in Multimodal Behavioral Analytics for Early Dementia Diagnosis: A Review

Chathurika Jayangani Palliya Guruge, Sharon L. Oviatt, Pari Delir Haghighi, Elizabeth Pritchard

Abstract: Clinical diagnosis of dementia is typically delayed and limited in accuracy, despite assessing cognitive impairments through neurological exams, brain imaging, and functional tests such as Activities of Daily Living. Recent advances in digital health and multimodal behavioral analytics are beginning to provide more sensitive, objective, unobtrusive and continuous assessment of functional abilities while people remain in a familiar setting such as their home, at work, or in the community. These new techniques analyze natural behaviors like speech, language, gait, eye gaze, hand movements, and facial expressions. This review compares existing clinical assessment methods with emerging behavioral analytic techniques that offer powerful capabilities for earlier and more precise diagnosis of dementia. It summarizes state-of-the-art multimodal behavioral analytics research for dementia diagnosis, including predictive features present in different human behaviors and the performance advantages of combining them into multimodal diagnostic systems. The many behavioral predictors documented in the literature are interpreted as deriving from six common cognitive deficits that are well known hallmarks of dementia. The review also discusses long-term trends in multimodal behavioral analytics research, and the five main areas requiring future work to realize the promise of earlier, more accurate, and widely accessible dementia diagnostic systems.