How to Select Balance Measures Sensitive to Parkinson’s Disease from Body-Worn Inertial Sensors—Separating the Trees from the Forest

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 t...

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Published in:Sensors (Basel, Switzerland) Vol. 19; no. 15; p. 3320
Main Authors: Hasegawa, Naoya, Shah, Vrutangkumar V., Carlson-Kuhta, Patricia, Nutt, John G., Horak, Fay B., Mancini, Martina
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 28.07.2019
MDPI
Subjects:
Aged
Aged, 80 and over
Algorithms
Balance
balance dysfunction
Falls
feature selection
Female
Gait
Gait - physiology
Humans
inertial measurement unit
Laboratories
Male
Middle Aged
Motion capture
objective measures
Parkinson Disease - diagnosis
Parkinson Disease - physiopathology
Parkinson's disease
Postural Balance - physiology
Posture
Severity of Illness Index
Standing Position
Systematic review
Walking
Wearable Electronic Devices
wearable technology
inertial measurement unit
balance dysfunction
wearable technology
objective measures
Parkinson’s disease
feature selection
ISSN:1424-8220, 1424-8220
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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.
AbstractList 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.
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.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.
Author Hasegawa, Naoya
Shah, Vrutangkumar V.
Mancini, Martina
Nutt, John G.
Carlson-Kuhta, Patricia
Horak, Fay B.
AuthorAffiliation Department of Neurology, Oregon Health & Science University, Portland, OR 97239-3098, USA
AuthorAffiliation_xml – name: Department of Neurology, Oregon Health & Science University, Portland, OR 97239-3098, USA
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  surname: Hasegawa
  fullname: Hasegawa, Naoya
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  givenname: Vrutangkumar V.
  surname: Shah
  fullname: Shah, Vrutangkumar V.
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  givenname: Patricia
  orcidid: 0000-0002-5794-4155
  surname: Carlson-Kuhta
  fullname: Carlson-Kuhta, Patricia
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  givenname: John G.
  surname: Nutt
  fullname: Nutt, John G.
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  givenname: Fay B.
  surname: Horak
  fullname: Horak, Fay B.
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  givenname: Martina
  surname: Mancini
  fullname: Mancini, Martina
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31357742$$D View this record in MEDLINE/PubMed
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Issue 15
Keywords inertial measurement unit
balance dysfunction
wearable technology
objective measures
Parkinson’s disease
feature selection
Language English
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Snippet This study aimed to determine the most sensitive objective measures of balance dysfunction that differ between people with Parkinson’s Disease (PD) and healthy...
This study aimed to determine the most sensitive objective measures of balance dysfunction that differ between people with Parkinson's Disease (PD) and healthy...
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StartPage 3320
SubjectTerms Aged
Aged, 80 and over
Algorithms
Balance
balance dysfunction
Falls
feature selection
Female
Gait
Gait - physiology
Humans
inertial measurement unit
Laboratories
Male
Middle Aged
Motion capture
objective measures
Parkinson Disease - diagnosis
Parkinson Disease - physiopathology
Parkinson's disease
Postural Balance - physiology
Posture
Severity of Illness Index
Standing Position
Systematic review
Walking
Wearable Electronic Devices
wearable technology
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Title How to Select Balance Measures Sensitive to Parkinson’s Disease from Body-Worn Inertial Sensors—Separating the Trees from the Forest
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