Parkinson's Disease Wearable Gait Analysis: Kinematic and Dynamic Markers for Diagnosis

Sensors (Basel). 2022 Nov 13;22(22):8773. doi: 10.3390/s22228773.

Abstract

Introduction: Gait features differ between Parkinson's disease (PD) and healthy subjects (HS). Kinematic alterations of gait include reduced gait speed, swing time, and stride length between PD patients and HS. Stride time and swing time variability are increased in PD patients with respect to HS. Additionally, dynamic parameters of asymmetry of gait are significantly different among the two groups. The aim of the present study is to evaluate which kind of gait analysis (dynamic or kinematic) is more informative to discriminate PD and HS gait features. Methods: In the present study, we analyzed gait dynamic and kinematic features of 108 PD patients and 88 HS from four cohorts of two datasets. Results: Kinematic features showed statistically significant differences among PD patients and HS for gait speed and time Up and Go test and for selected kinematic dispersion indices (standard deviation and interquartile range of swing, stance, and double support time). Dynamic features did not show any statistically significant difference between PD patients and HS. Discussion: Despite kinematics features like acceleration being directly proportional to dynamic features like ground reaction force, the results of this study showed the so-called force/rhythm dichotomy since kinematic features were more informative than dynamic ones.

Keywords: Parkinson’s disease; diagnosis; dynamic analysis; gait analysis; kinematic analysis; wearable.

MeSH terms

  • Biomarkers
  • Biomechanical Phenomena
  • Gait
  • Humans
  • Parkinson Disease* / diagnosis
  • Postural Balance
  • Time and Motion Studies
  • Wearable Electronic Devices*

Substances

  • Biomarkers

Grants and funding

This research received no external funding.