Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Daniel Janssen
- Wolfgang I Schoellhorn
- Karl M Newell
- Joerg M Jaeger
- Franz Rost
- Katrin Vehof
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000295996100011&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1016/j.humov.2010.08.010
- eISSN
- 1872-7646
- Externe Identifier
- Clarivate Analytics Document Solution ID: 834WV
- PubMed Identifier: 21195495
- ISSN
- 0167-9457
- Ausgabe der Veröffentlichung
- 5
- Zeitschrift
- HUMAN MOVEMENT SCIENCE
- Schlüsselwörter
- Fatigue
- Gait
- Pattern recognition
- Support vector machine
- Neural network
- Paginierung
- 966 - 975
- Datum der Veröffentlichung
- 2011
- Status
- Published
- Titel
- Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps
- Sub types
- Article
- Ausgabe der Zeitschrift
- 30
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Autoren
- Daniel Janssen
- Wolfgang I Schöllhorn
- Karl M Newell
- Jörg M Jäger
- Franz Rost
- Katrin Vehof
- DOI
- 10.1016/j.humov.2010.08.010
- ISSN
- 0167-9457
- Ausgabe der Veröffentlichung
- 5
- Zeitschrift
- Human Movement Science
- Sprache
- en
- Paginierung
- 966 - 975
- Datum der Veröffentlichung
- 2011
- Status
- Published
- Herausgeber
- Elsevier BV
- Herausgeber URL
- http://dx.doi.org/10.1016/j.humov.2010.08.010
- Datum der Datenerfassung
- 2018
- Titel
- Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps
- Ausgabe der Zeitschrift
- 30
Datenquelle: Crossref
- Abstract
- The aim of the study was to train and test support vector machines (SVM) and self-organizing maps (SOM) to correctly classify gait patterns before, during and after complete leg exhaustion by isokinetic leg exercises. Ground reaction forces were derived for 18 gait cycles on 9 adult participants. Immediately before the trials 7-12, participants were required to completely exhaust their calves with the aid of additional weights (44.4±8.8kg). Data were analyzed using: (a) the time courses directly and (b) only the deviations from each individual's calculated average gait pattern. On an inter-individual level the person recognition of the gait patterns was 100% realizable. Fatigue recognition was also highly probable at 98.1%. Additionally, applied SOMs allowed an alternative visualization of the development of fatigue in the gait patterns over the progressive fatiguing exercise regimen.
- Addresses
- Training and Movement Science, University of Mainz, Albert Schweitzer Strasse 22, 55099 Mainz, Germany. djanssen@uni-mainz.de
- Autoren
- Daniel Janssen
- Wolfgang I Schöllhorn
- Karl M Newell
- Jörg M Jäger
- Franz Rost
- Katrin Vehof
- DOI
- 10.1016/j.humov.2010.08.010
- eISSN
- 1872-7646
- Externe Identifier
- PubMed Identifier: 21195495
- Open access
- false
- ISSN
- 0167-9457
- Ausgabe der Veröffentlichung
- 5
- Zeitschrift
- Human movement science
- Schlüsselwörter
- Humans
- Gait
- Individuality
- Muscle Fatigue
- Nonlinear Dynamics
- Weight Lifting
- Pattern Recognition, Automated
- Adult
- Male
- Young Adult
- Biomechanical Phenomena
- Support Vector Machine
- Sprache
- eng
- Medium
- Print-Electronic
- Online publication date
- 2010
- Paginierung
- 966 - 975
- Datum der Veröffentlichung
- 2011
- Status
- Published
- Datum der Datenerfassung
- 2011
- Titel
- Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 30
Datenquelle: Europe PubMed Central
- Abstract
- The aim of the study was to train and test support vector machines (SVM) and self-organizing maps (SOM) to correctly classify gait patterns before, during and after complete leg exhaustion by isokinetic leg exercises. Ground reaction forces were derived for 18 gait cycles on 9 adult participants. Immediately before the trials 7-12, participants were required to completely exhaust their calves with the aid of additional weights (44.4±8.8kg). Data were analyzed using: (a) the time courses directly and (b) only the deviations from each individual's calculated average gait pattern. On an inter-individual level the person recognition of the gait patterns was 100% realizable. Fatigue recognition was also highly probable at 98.1%. Additionally, applied SOMs allowed an alternative visualization of the development of fatigue in the gait patterns over the progressive fatiguing exercise regimen.
- Date of acceptance
- 2010
- Autoren
- Daniel Janssen
- Wolfgang I Schöllhorn
- Karl M Newell
- Jörg M Jäger
- Franz Rost
- Katrin Vehof
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/21195495
- DOI
- 10.1016/j.humov.2010.08.010
- eISSN
- 1872-7646
- Ausgabe der Veröffentlichung
- 5
- Zeitschrift
- Hum Mov Sci
- Schlüsselwörter
- Adult
- Biomechanical Phenomena
- Gait
- Humans
- Individuality
- Male
- Muscle Fatigue
- Nonlinear Dynamics
- Pattern Recognition, Automated
- Support Vector Machine
- Weight Lifting
- Young Adult
- Sprache
- eng
- Country
- Netherlands
- Paginierung
- 966 - 975
- PII
- S0167-9457(10)00147-8
- Datum der Veröffentlichung
- 2011
- Status
- Published
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2012
- Titel
- Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 30
Datenquelle: PubMed
- Beziehungen:
- Eigentum von