Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Fabian Horst
  • Alexander Eekhoff
  • Karl M Newell
  • Wolfgang Schöllhorn
Sammlungen
  • metadata
ISSN
1932-6203
Ausgabe der Veröffentlichung
6
Zeitschrift
PLoS one
Schlüsselwörter
  • 000 Allgemeines
  • 000 Generalities
Sprache
eng
Paginierung
e0179738
Datum der Veröffentlichung
2017
Herausgeber
PLoS
Herausgeber URL
http://dx.doi.org/10.1371/journal.pone.0179738
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression
Ausgabe der Zeitschrift
12

Datenquelle: METADATA.UB

Andere Metadatenquellen:
Autoren
  • Fabian Horst
  • Alexander Eekhoff
  • Karl M Newell
  • Wolfgang I Schollhorn
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000403364600057&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1371/journal.pone.0179738
Externe Identifier
  • Clarivate Analytics Document Solution ID: EX6PQ
  • PubMed Identifier: 28617842
ISSN
1932-6203
Ausgabe der Veröffentlichung
6
Zeitschrift
PLOS ONE
Artikelnummer
ARTN e0179738
Datum der Veröffentlichung
2017
Status
Published
Titel
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression
Sub types
  • Article
Ausgabe der Zeitschrift
12

Datenquelle: Web of Science (Lite)

Autoren
  • Fabian Horst
  • Alexander Eekhoff
  • Karl M Newell
  • Wolfgang I Schöllhorn
DOI
10.1371/journal.pone.0179738
Editoren
  • Yih-Kuen Jan
eISSN
1932-6203
Ausgabe der Veröffentlichung
6
Zeitschrift
PLOS ONE
Sprache
en
Online publication date
2017
Paginierung
e0179738 - e0179738
Status
Published online
Herausgeber
Public Library of Science (PLoS)
Herausgeber URL
http://dx.doi.org/10.1371/journal.pone.0179738
Datum der Datenerfassung
2019
Titel
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression
Ausgabe der Zeitschrift
12

Datenquelle: Crossref

Abstract
<h4>Objective</h4>Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours).<h4>Methods</h4>Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns.<h4>Results and discussion</h4>Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales.<h4>Conclusion</h4>Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.
Addresses
  • Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Mainz, Rhineland-Palatinate, Germany.
Autoren
  • Fabian Horst
  • Alexander Eekhoff
  • Karl M Newell
  • Wolfgang I Schöllhorn
DOI
10.1371/journal.pone.0179738
eISSN
1932-6203
Externe Identifier
  • PubMed Identifier: 28617842
  • PubMed Central ID: PMC5472314
Open access
true
ISSN
1932-6203
Ausgabe der Veröffentlichung
6
Zeitschrift
PloS one
Schlüsselwörter
  • Humans
  • Gait
  • Learning
  • Models, Biological
  • Adult
  • Female
  • Male
Sprache
eng
Medium
Electronic-eCollection
Online publication date
2017
Open access status
Open Access
Paginierung
e0179738
Datum der Veröffentlichung
2017
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2017
Titel
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.
Sub types
  • Clinical Trial
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
12

Files

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0179738&type=printable https://europepmc.org/articles/PMC5472314?pdf=render

Datenquelle: Europe PubMed Central

Abstract
OBJECTIVE: Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). METHODS: Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. RESULTS AND DISCUSSION: Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. CONCLUSION: Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.
Date of acceptance
2017
Autoren
  • Fabian Horst
  • Alexander Eekhoff
  • Karl M Newell
  • Wolfgang I Schöllhorn
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/28617842
DOI
10.1371/journal.pone.0179738
eISSN
1932-6203
Externe Identifier
  • PubMed Central ID: PMC5472314
Ausgabe der Veröffentlichung
6
Zeitschrift
PLoS One
Schlüsselwörter
  • Adult
  • Female
  • Gait
  • Humans
  • Learning
  • Male
  • Models, Biological
Sprache
eng
Country
United States
Paginierung
e0179738
PII
PONE-D-17-03243
Datum der Veröffentlichung
2017
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2017
Titel
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.
Sub types
  • Clinical Trial
  • Journal Article
Ausgabe der Zeitschrift
12

Datenquelle: PubMed

Author's licence
CC-BY
Autoren
  • Fabian Horst
  • Alexander Eekhoff
  • Karl M Newell
  • Wolfgang I Schöllhorn
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • DFG-OA-Publizieren (2012 - 2017)
Resource version
Published version
DOI
10.1371/journal.pone.0179738
Funding acknowledgements
  • DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin
File(s) embargoed
false
Open access
true
ISSN
1932-6203
Ausgabe der Veröffentlichung
6
Zeitschrift
PLoS one
Schlüsselwörter
  • 610 Medizin
  • 610 Medical sciences
  • 796 Sport
  • 796 Athletic and outdoor sports and games
Sprache
eng
Open access status
Open Access
Paginierung
e0179738
Datum der Veröffentlichung
2017
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/7169
Herausgeber
PLoS
Herausgeber URL
http://dx.doi.org/10.1371/journal.pone.0179738
Datum der Datenerfassung
2022
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Zugang
Public
Titel
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression
Ausgabe der Zeitschrift
12

Files

intraindividual_gait_patterns-20220612164204564.pdf

Datenquelle: OPENSCIENCE.UB

Abstract
OBJECTIVE Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). METHODS Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. RESULTS AND DISCUSSION Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 $\pm$ 8.8% and 86.3 $\pm$ 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. CONCLUSION Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.
Autoren
  • Fabian Horst
  • Alexander Eekhoff
  • Karl M Newell
  • Wolfgang I Schöllhorn
DOI
10.1371/journal.pone.0179738
Zeitschrift
PloS one
Notes
file: http://www.ncbi.nlm.nih.gov/pubmed/28617842 file: http://www.ncbi.nlm.nih.gov/pubmed/28617842
Artikelnummer
6
Paginierung
e0179738 - e0179738
Datum der Veröffentlichung
2017
Datum der Datenerfassung
2021
Titel
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression
Sub types
  • article
Ausgabe der Zeitschrift
12

Datenquelle: Manual

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