Continuous assessment of gait velocity in Parkinson's disease from unobtrusive measurements

Misha Pavel, Tamara Hayes, Ishan Tsay, Deniz Erdogmus, Anindya Paul, Nicole Larimer, Holly Jimison, John Nutt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

The ability to assess the neurological state of patients with neurodegenerative diseases on a continuous basis is an important component of future care for these chronically ill patients. In this paper we describe a set of algorithms to infer gait velocity and its variability using data from an unobtrusive sensor network by incorporating a simple dynamic description of a patient's movements within his or her residence. The sensors include a combination of passive motion detectors and active radio frequency identification tags. The dynamic model is a simple 4 state hidden Markov model. We investigated the ability of this model to assess gait velocity and its variability using data from a six month pilot study of several patients with early stage Parkinson's disease.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
Pages700-703
Number of pages4
DOIs
StatePublished - Sep 25 2007
Event3rd International IEEE EMBS Conference on Neural Engineering - Kohala Coast, HI, United States
Duration: May 2 2007May 5 2007

Publication series

NameProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering

Other

Other3rd International IEEE EMBS Conference on Neural Engineering
CountryUnited States
CityKohala Coast, HI
Period5/2/075/5/07

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Neuroscience (miscellaneous)

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  • Cite this

    Pavel, M., Hayes, T., Tsay, I., Erdogmus, D., Paul, A., Larimer, N., Jimison, H., & Nutt, J. (2007). Continuous assessment of gait velocity in Parkinson's disease from unobtrusive measurements. In Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (pp. 700-703). [4227374] (Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering). https://doi.org/10.1109/CNE.2007.369769