2026-04-29T04:08:46Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/93302025-05-27T08:48:51Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19614
Repisalud
author
Martínez-Martín, Pablo
author
Villagra, Federico
author
Castellote, Juan Manuel
author
Pastor, María A
funder
Instituto de Salud Carlos III
2020-03-25T13:03:52Z
2020-03-25T13:03:52Z
2018-12-01
Sensors (Basel). 2018 Dec 1;18(12). pii: E4224.
1424-8220
http://hdl.handle.net/20.500.12105/9330
30513798
10.3390/s18124224
1424-8220
Sensors (Basel, Switzerland)
The aim of this study is to compare the properties of free-walking at a natural pace between mild Parkinson's disease (PD) patients during the ON-clinical status and two control groups. In-shoe pressure-sensitive insoles were used to quantify the temporal and force characteristics of a 5-min free-walking in 11 PD patients, in 16 young healthy controls, and in 12 age-matched healthy controls. Inferential statistics analyses were performed on the kinematic and kinetic parameters to compare groups' performances, whereas feature selection analyses and automatic classification were used to identify the signature of parkinsonian gait and to assess the performance of group classification, respectively. Compared to healthy subjects, the PD patients' gait pattern presented significant differences in kinematic parameters associated with bilateral coordination but not in kinetics. Specifically, patients showed an increased variability in double support time, greater gait asymmetry and phase deviation, and also poorer phase coordination. Feature selection analyses based on the ReliefF algorithm on the differential parameters in PD patients revealed an effect of the clinical status, especially true in double support time variability and gait asymmetry. Automatic classification of PD patients, young and senior subjects confirmed that kinematic predictors produced a slightly better classification performance than kinetic predictors. Overall, classification accuracy of groups with a linear discriminant model which included the whole set of features (i.e., demographics and parameters extracted from the sensors) was 64.1%.
eng
Parkinson’s disease
Bilateral coordination
Free-walking
Kinematics
Kinetics
Machine learning
Pressure-sensitive insole sensors
Kinematic and Kinetic Patterns Related to Free-Walking in Parkinson's Disease
research article
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KinematicAndKineticPatterns_2018.pdf
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KinematicAndKineticPatterns_2018.pdf.txt