Modeling, detecting, and tracking freezing of gait in Parkinson disease using inertial sensors

G. V. Prateek, Isaac Skog, Marie E. McNeely, Ryan P. Duncan, Gammon M. Earhart, Arye Nehorai

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

In this paper, we develop new methods to automatically detect the onset and duration of freezing of gait (FOG) in people with Parkinson disease (PD) in real time, using inertial sensors. We first build a physical model that describes the trembling motion during the FOG events. Then, we design a generalized likelihood ratio test framework to develop a two-stage detector for determining the zero-velocity and trembling events during gait. Thereafter, to filter out falsely detected FOG events, we develop a point-process filter that combines the output of the detectors with information about the speed of the foot, provided by a foot-mounted inertial navigation system. We computed the probability of FOG by using the point-process filter to determine the onset and duration of the FOG event. Finally, we validate the performance of the proposed system design using real data obtained from people with PD who performed a set of gait tasks. We compare our FOG detection results with an existing method that only uses accelerometer data. The results indicate that our method yields 81.03% accuracy in detecting FOG events and a threefold decrease in the false-alarm rate relative to the existing method.

Original languageEnglish
Article number8231183
Pages (from-to)2152-2161
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume65
Issue number10
DOIs
StatePublished - Oct 2018

Keywords

  • Parkinson disease
  • accelerometer
  • freezing of gait
  • gyroscopes
  • inertial sensors
  • point-process filter

Fingerprint Dive into the research topics of 'Modeling, detecting, and tracking freezing of gait in Parkinson disease using inertial sensors'. Together they form a unique fingerprint.

Cite this