TY - JOUR
T1 - Automated Classification of Postural Control for Individuals with Parkinson’s Disease Using a Machine Learning Approach
T2 - A Preliminary Study
AU - Li, Yumeng
AU - Zhang, Shuqi
AU - Odeh, Christina
N1 - Publisher Copyright:
© 2020 Human Kinetics, Inc.
PY - 2020/10
Y1 - 2020/10
N2 - The purposes of the study were (1) to compare postural sway between participants with Parkinson's disease (PD) and healthy controls and (2) to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie, logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbormethod exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.
AB - The purposes of the study were (1) to compare postural sway between participants with Parkinson's disease (PD) and healthy controls and (2) to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie, logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbormethod exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.
KW - Balance
KW - Center of pressure
KW - Elderly
KW - Machine learning classifier
KW - Postural stability
UR - http://www.scopus.com/inward/record.url?scp=85092901209&partnerID=8YFLogxK
UR - https://scholarworks.boisestate.edu/kinesiology_facpubs/220
U2 - 10.1123/JAB.2019-0400
DO - 10.1123/JAB.2019-0400
M3 - Article
C2 - 32736341
SN - 1065-8483
VL - 36
SP - 334
EP - 339
JO - Journal of Applied Biomechanics
JF - Journal of Applied Biomechanics
IS - 5
ER -