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Related Experiment Video

Updated: May 14, 2026

3D Kinematic Gait Analysis for Preclinical Studies in Rodents
10:19

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

Published on: August 3, 2019

Automated motion sensor quantification of gait and lower extremity bradykinesia.

Dustin A Heldman1, Danielle E Filipkowski, David E Riley

  • 1Great Lakes NeuroTechnologies Inc., Cleveland, OH 44125, USA. dheldman@glneurotech.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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New algorithms accurately quantify Parkinson's disease (PD) gait and bradykinesia using heel-worn sensors. This technology enables reliable home-based monitoring of motor symptoms for improved patient care.

Area of Science:

  • Biomedical Engineering
  • Neurology
  • Movement Science

Background:

  • Parkinson's disease (PD) diagnosis and monitoring rely on clinical assessments.
  • Quantifying motor symptoms like bradykinesia and gait disturbances is crucial for PD management.
  • Objective, quantitative measures are needed to complement subjective clinical evaluations.

Purpose of the Study:

  • To develop and validate algorithms for quantifying gait and lower extremity bradykinesia in Parkinson's disease patients.
  • To utilize data from a novel heel-worn motion sensor unit for symptom assessment.
  • To create a system for accessible, home-based monitoring of Parkinson's disease motor symptoms.

Main Methods:

  • Development of algorithms using kinematic data from a heel-worn motion sensor.

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Methods to Quantify Pharmacologically Induced Alterations in Motor Function in Human Incomplete SCI
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Last Updated: May 14, 2026

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Published on: August 3, 2019

Methods to Quantify Pharmacologically Induced Alterations in Motor Function in Human Incomplete SCI
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Methods to Quantify Pharmacologically Induced Alterations in Motor Function in Human Incomplete SCI

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  • Correlation of sensor-derived data with clinical scores from three movement disorder neurologists.
  • Utilizing the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) for clinical evaluation.
  • Application of multiple linear regression models to analyze kinematic and clinical data.

    • Main Results:

    • Algorithms demonstrated high correlation with clinician scores, achieving an average correlation coefficient of 0.86.
    • The developed models accurately quantified gait and bradykinesia.
    • Successful integration of the algorithms into a home-based monitoring system.

    Conclusions:

    • The developed algorithms provide a reliable and objective method for quantifying gait and bradykinesia in Parkinson's disease.
    • The heel-worn motion sensor system offers a promising tool for continuous, home-based monitoring of PD motor symptoms.
    • This technology has the potential to improve clinical management and patient outcomes in Parkinson's disease.