title: Model-based inference of cognitive processes from unobtrusive gait velocity measurements.
abstract: In this paper we describe a preliminary modeling and analysis of a unique data set comprising unobtrusive and continuous measurements of gait velocity in the elder participants' residences. The data have been collected as a part of a longitudinal study aimed at early detection of cognitive decline. We motivate these analyses by first presenting evidence that suggests significant relationship between gait parameters and cognitive functions. We then describe a simple, model-based approach to the analysis of gait velocity using a weighted correlation function estimates. One of the main challenges is due to the fact that the daily estimates of the gait parameters vary with the number of walks. We illustrate the importance of using weighted as opposed to unweighted estimates on a sample of different houses. The correlation functions appear to capture behavioral differences that can be related to the cognitive functioning of the participants.
authors.affiliation: Department of Biomedical Engineering, Oregon Health & Science University. 3303 SW Bond Avenue, Portland, OR 97239, USA. austidan@bme.ogi.edu
authors.forename: Daniel
authors.initials: D
authors.lastname: Austin
authors.forename: Todd
authors.initials: T
authors.lastname: Leen
authors.forename: Tamara L
authors.initials: TL
authors.lastname: Hayes
authors.forename: Jeff
authors.initials: J
authors.lastname: Kaye
authors.forename: Holly
authors.initials: H
authors.lastname: Jimison
authors.forename: Nora
authors.initials: N
authors.lastname: Mattek
authors.forename: Misha
authors.initials: M
authors.lastname: Pavel
mesh_terms: D000054:Acceleration; D003071:Cognition / Q000502:physiology*; D005684:Gait / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D018670:Monitoring, Ambulatory / Q000379:methods*; D013997:Time Factors; D016138:Walking / Q000502:physiology