The adaptation of GDL motion recognition system to sport and rehabilitation techniques analysis
Identifieur interne : 000048 ( Main/Exploration ); précédent : 000047; suivant : 000049The adaptation of GDL motion recognition system to sport and rehabilitation techniques analysis
Auteurs : Tomasz Hachaj [Pologne] ; Marek R. Ogiela [Pologne]Source :
- Journal of Medical Systems [ 0148-5598 ] ; 2016.
Abstract
The main novelty of this paper is presenting the adaptation of Gesture Description Language (GDL) methodology to sport and rehabilitation data analysis and classification. In this paper we showed that Lua language can be successfully used for adaptation of the GDL classifier to those tasks. The newly applied scripting language allows easily extension and integration of classifier with other software technologies and applications. The obtained execution speed allows using the methodology in the real-time motion capture data processing where capturing frequency differs from 100 Hz to even 500 Hz depending on number of features or classes to be calculated and recognized. Due to this fact the proposed methodology can be used to the high-end motion capture system. We anticipate that using novel, efficient and effective method will highly help both sport trainers and physiotherapist in they practice. The proposed approach can be directly applied to motion capture data kinematics analysis (evaluation of motion without regard to the forces that cause that motion). The ability to apply pattern recognition methods for GDL description can be utilized in virtual reality environment and used for sport training or rehabilitation treatment.
Url:
DOI: 10.1007/s10916-016-0493-6
PubMed: 27106581
PubMed Central: 4841835
Affiliations:
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Ogiela</name><affiliation wicri:level="1"><nlm:aff id="Aff2">Cryptography and Cognitive Informatics Research Group, AGH University of Science and Technology, 30 Mickiewicza Ave, 30-059 Krakow, Poland</nlm:aff><country xml:lang="fr">Pologne</country><wicri:regionArea>Cryptography and Cognitive Informatics Research Group, AGH University of Science and Technology, 30 Mickiewicza Ave, 30-059 Krakow</wicri:regionArea><wicri:noRegion>30-059 Krakow</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of Medical Systems</title><idno type="ISSN">0148-5598</idno><idno type="eISSN">1573-689X</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The main novelty of this paper is presenting the adaptation of Gesture Description Language (GDL) methodology to sport and rehabilitation data analysis and classification. In this paper we showed that Lua language can be successfully used for adaptation of the GDL classifier to those tasks. The newly applied scripting language allows easily extension and integration of classifier with other software technologies and applications. The obtained execution speed allows using the methodology in the real-time motion capture data processing where capturing frequency differs from 100 Hz to even 500 Hz depending on number of features or classes to be calculated and recognized. Due to this fact the proposed methodology can be used to the high-end motion capture system. We anticipate that using novel, efficient and effective method will highly help both sport trainers and physiotherapist in they practice. The proposed approach can be directly applied to motion capture data kinematics analysis (evaluation of motion without regard to the forces that cause that motion). The ability to apply pattern recognition methods for GDL description can be utilized in virtual reality environment and used for sport training or rehabilitation treatment.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, Q" uniqKey="Zhang Q">Q Zhang</name></author><author><name sortKey="Song, X" uniqKey="Song X">X Song</name></author><author><name sortKey="Shibasaki, R" uniqKey="Shibasaki R">R Shibasaki</name></author><author><name sortKey="Zhao, H" uniqKey="Zhao H">H Zhao</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Schwarz, La" uniqKey="Schwarz L">LA Schwarz</name></author><author><name sortKey="Mkhitaryan, A" uniqKey="Mkhitaryan A">A Mkhitaryan</name></author><author><name sortKey="Mateus, D" uniqKey="Mateus D">D Mateus</name></author><author><name sortKey="Navab, N" uniqKey="Navab N">N 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Hachaj</name></author><author><name sortKey="Ogiela, Mr" uniqKey="Ogiela M">MR Ogiela</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Pologne</li></country></list><tree><country name="Pologne"><noRegion><name sortKey="Hachaj, Tomasz" sort="Hachaj, Tomasz" uniqKey="Hachaj T" first="Tomasz" last="Hachaj">Tomasz Hachaj</name></noRegion><name sortKey="Ogiela, Marek R" sort="Ogiela, Marek R" uniqKey="Ogiela M" first="Marek R." last="Ogiela">Marek R. 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