Oswald Barral, Giuseppe Carenini, Cristina Conati, Thalia S. Field, Zoe O’Neill and Tom Soroski, 3RD WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI’19, 10-12 AUGUST
This paper presents a novel approach to investigate the utility of combined spontaneous speech and eye tracking data as clinical markers for early detection of Alzheimer’s Disease (AD). We describe an experimental protocol in which we record eye movements and speech data from AD patients and age- matched healthy controls while they perform the standard Boston Cookie Theft picture description task.
We present directions on how to build predictors of AD by leveraging the multimodal nature of the task, and discuss the expected outcomes of the work. We wrap up the paper with a discussion on some of the main implications that this type of approach brings to the field of AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL).
An accessible, non-invasive and accurate means to risk-stratify individuals with subjective memory complaints could lead to enhanced diagnostic efficiency.
We presented an experimental protocol and data analysis approach to investigate the utility of combined spontaneous speech and eye tracking data as clinical markers for early detection of AD.