Model-based inference of cognitive processes from unobtrusive gait velocity measurements.

Daniel Austin, Todd Leen, Tamara L. Hayes, Jeff Kaye, Holly Jimison, Nora Mattek, Misha Pavel

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)5230-5233
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics


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