Validation of a new model-free signal processing method for gait feature extraction using inertial measurement units to diagnose and quantify the severity of Parkinson's disease

Ali Akbari, Richard B. Dewey, Roozbeh Jafari

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

Abstract

Gait analysis is important in diagnosing and quantifying the severity of Parkinson's disease. Different motion tracking systems such as inertial measurement units (IMU) are widely used to detect gait parameters associated with the severity of Parkinson's disease. Although these systems are accurate enough to measure different gait parameters, they utilize a predefined model of human gait to measure these parameters. Model- based signal processing, that takes into account the kinematics of human body, enforces that sensors be placed in a certain configuration in terms of orientation and location which introduces a burden at the signal processing development phase. In addition, it affects the accuracy and robustness of the system when the user does not place the sensors at their pre-defined locations and with a pre-define orientation. In this paper, we introduce a set of model-free features to estimate gait parameters for the applications of diagnosing and quantifying the severity of Parkinson's disease. A model-free signal processing technique does not limit sensor placement, in addition, it does not require the knowledge on the kinematics of the users and the human subjects. We show that our proposed features, using a model-free signal processing technique, are highly correlated (R-value up to 0.96 for suitable locations) with gait parameters obtained from model-based sophisticated algorithms. Therefore, these simple model-free features may be suitable for ongoing assessment of Parkinson's disease and they can be an alternative for conventional gait parameters used for rapid application development.

Original languageEnglish (US)
Title of host publication2017 26th International Conference on Computer Communications and Networks, ICCCN 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509029914
DOIs
StatePublished - Sep 14 2017
Event26th International Conference on Computer Communications and Networks, ICCCN 2017 - Vancouver, Canada
Duration: Jul 31 2017Aug 3 2017

Publication series

Name2017 26th International Conference on Computer Communications and Networks, ICCCN 2017

Other

Other26th International Conference on Computer Communications and Networks, ICCCN 2017
CountryCanada
CityVancouver
Period7/31/178/3/17

Keywords

  • Gait parameters
  • IMU
  • Modelfree features
  • Parkinson's disease

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software
  • Management of Technology and Innovation
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Control and Optimization

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    Akbari, A., Dewey, R. B., & Jafari, R. (2017). Validation of a new model-free signal processing method for gait feature extraction using inertial measurement units to diagnose and quantify the severity of Parkinson's disease. In 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017 [8038414] (2017 26th International Conference on Computer Communications and Networks, ICCCN 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCCN.2017.8038414