How to select balance measures sensitive to parkinson’s disease from body-worn inertial sensors—separating the trees from the forest

Naoya Hasegawa, Vrutangkumar V. Shah, Patricia Carlson-Kuhta, John G. Nutt, Fay B. Horak, Martina Mancini

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

This study aimed to determine the most sensitive objective measures of balance dysfunction that differ between people with Parkinson’s Disease (PD) and healthy controls. One-hundred and forty-four people with PD and 79 age-matched healthy controls wore eight inertial sensors while performing tasks to measure five domains of balance: standing posture (Sway), anticipatory postural adjustments (APAs), automatic postural responses (APRs), dynamic posture (Gait) and limits of stability (LOS). To reduce the initial 93 measures, we selected uncorrelated measures that were most sensitive to PD. After applying a threshold on the Standardized Mean Difference between PD and healthy controls, 44 measures remained; and after reducing highly correlated measures, 24 measures remained. The four most sensitive measures were from APAs and Gait domains. The random forest with 10-fold cross-validation on the remaining measures (n = 24) showed an accuracy to separate PD from healthy controls of 82.4%—identical to result for all measures. Measures from the most sensitive domains, APAs and Gait, were significantly correlated with the severity of disease and with patient-related outcomes. This method greatly reduced the objective measures of balance to the most sensitive for PD, while still capturing four of the five domains of balance.

Original languageEnglish (US)
Article number3320
JournalSensors (Switzerland)
Volume19
Issue number15
DOIs
StatePublished - Aug 1 2019

Keywords

  • Balance dysfunction
  • Feature selection
  • Inertial measurement unit
  • Objective measures
  • Parkinson’s disease
  • Wearable technology

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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