A topological approach for human movement classification and anticipation
dc.contributor.author | BENSEKKA, Chakib Arslane |
dc.contributor.author | GUILLET, Christophe |
dc.contributor.author
hal.structure.identifier | POZZO, Thierry
|
dc.contributor.author
hal.structure.identifier | MERIENNE, Frédéric
|
dc.date.accessioned | 2018 |
dc.date.available | 2018 |
dc.date.issued | 2017 |
dc.date.submitted | 2018 |
dc.identifier.issn | 0966-6362 |
dc.identifier.uri | http://hdl.handle.net/10985/12777 |
dc.description.abstract | The motion capture systems are increasingly used for biomedical purposes. In order to recognize and classify the movements, however whole-body movements using passive markers, generate a huge amount of data. Can topological data analysis methods improve the recognition of movements? Can we use the results of this analysis combined with particular types of neural networks to anticipate the continuation of a movement? |
dc.language.iso | en |
dc.publisher | Elsevier |
dc.rights | Post-print |
dc.subject | Human movement analysis |
dc.subject | Anticipation |
dc.title | A topological approach for human movement classification and anticipation |
ensam.embargo.terms | 2018-04-07 |
dc.identifier.doi | 10.1016/j.gaitpost.2017.06.387 |
dc.typdoc | Communication avec acte |
dc.localisation | Centre de Châlons-en-Champagne |
dc.localisation | Institut de Chalon sur Saône |
dc.subject.hal | Mathématique: Topologie géométrique |
dc.subject.hal | Informatique: Intelligence artificielle |
dc.subject.hal | Informatique: Synthèse d'image et réalité virtuelle |
ensam.audience | Internationale |
ensam.conference.title | ESMAC 2017 |
ensam.conference.date | 2017-09-06 |
ensam.country | Norvège |
ensam.title.proceeding | Gait & Posture |
ensam.page | 229-230 |
ensam.volume | 57 |
ensam.city | Trondheim |
ensam.peerReviewing | Oui |
ensam.invitedCommunication | Non |
ensam.proceeding | Oui |
hal.identifier | hal-01588554 |
hal.version | 1 |
hal.status | accept |