Generating shared latent variables for robots to imitate human movements and understand their physical limitations

Maxime Devanne; Sao Mai Nguyen

doi:10.1007/978-3-030-11012-3_15

Generating shared latent variables for robots to imitate human movements and understand their physical limitations

Maxime Devanne
, Sao Mai Nguyen

LAB-STICC

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition, we propose to extend the model to adapt the robots’ understanding to patients’ physical limitations during assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to patients’ limitations.

langue originale	Anglais
titre	Computer Vision – ECCV 2018 Workshops, Proceedings
rédacteurs en chef	Stefan Roth, Laura Leal-Taixé
Editeur	Springer Verlag
Pages	190-197
Nombre de pages	8
ISBN (imprimé)	9783030110116
Les DOIs	https://doi.org/10.1007/978-3-030-11012-3_15
état	Publié - 1 janv. 2019
Evénement	15th European Conference on Computer Vision, ECCV 2018 - Munich, Allemagne Durée: 8 sept. 2018 → 14 sept. 2018

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11130 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	15th European Conference on Computer Vision, ECCV 2018
Pays/Territoire	Allemagne
La ville	Munich
période	8/09/18 → 14/09/18

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 3 Bonne santé et bien-être

Accès au document

10.1007/978-3-030-11012-3_15

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Devanne, M., & Nguyen, S. M. (2019). Generating shared latent variables for robots to imitate human movements and understand their physical limitations. Dans S. Roth, & L. Leal-Taixé (eds.), Computer Vision – ECCV 2018 Workshops, Proceedings (p. 190-197). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 11130 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-11012-3_15

Devanne, Maxime ; Nguyen, Sao Mai. / Generating shared latent variables for robots to imitate human movements and understand their physical limitations. Computer Vision – ECCV 2018 Workshops, Proceedings. Editeur / Stefan Roth ; Laura Leal-Taixé. Springer Verlag, 2019. p. 190-197 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4f809310ce0142debe16c3762d172d84,

title = "Generating shared latent variables for robots to imitate human movements and understand their physical limitations",

abstract = "Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition, we propose to extend the model to adapt the robots{\textquoteright} understanding to patients{\textquoteright} physical limitations during assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to patients{\textquoteright} limitations.",

keywords = "Motion analysis, Physical rehabilitation, Robot imitation, Shared gaussian process latent variable model, Transfer knowledge",

author = "Maxime Devanne and Nguyen, \{Sao Mai\}",

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Devanne, M & Nguyen, SM 2019, Generating shared latent variables for robots to imitate human movements and understand their physical limitations. Dans S Roth & L Leal-Taixé (eds), Computer Vision – ECCV 2018 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 11130 LNCS, Springer Verlag, p. 190-197, 15th European Conference on Computer Vision, ECCV 2018, Munich, Allemagne, 8/09/18. https://doi.org/10.1007/978-3-030-11012-3_15

Generating shared latent variables for robots to imitate human movements and understand their physical limitations. / Devanne, Maxime; Nguyen, Sao Mai.
Computer Vision – ECCV 2018 Workshops, Proceedings. Ed. / Stefan Roth; Laura Leal-Taixé. Springer Verlag, 2019. p. 190-197 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 11130 LNCS).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Generating shared latent variables for robots to imitate human movements and understand their physical limitations

AU - Devanne, Maxime

AU - Nguyen, Sao Mai

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition, we propose to extend the model to adapt the robots’ understanding to patients’ physical limitations during assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to patients’ limitations.

AB - Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition, we propose to extend the model to adapt the robots’ understanding to patients’ physical limitations during assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to patients’ limitations.

KW - Motion analysis

KW - Physical rehabilitation

KW - Robot imitation

KW - Shared gaussian process latent variable model

KW - Transfer knowledge

UR - https://www.scopus.com/pages/publications/85061830282

U2 - 10.1007/978-3-030-11012-3_15

DO - 10.1007/978-3-030-11012-3_15

M3 - Conference contribution

AN - SCOPUS:85061830282

SN - 9783030110116

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 190

EP - 197

BT - Computer Vision – ECCV 2018 Workshops, Proceedings

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A2 - Leal-Taixé, Laura

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T2 - 15th European Conference on Computer Vision, ECCV 2018

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Devanne M, Nguyen SM. Generating shared latent variables for robots to imitate human movements and understand their physical limitations. Dans Roth S, Leal-Taixé L, rédacteurs en chef, Computer Vision – ECCV 2018 Workshops, Proceedings. Springer Verlag. 2019. p. 190-197. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-11012-3_15

Generating shared latent variables for robots to imitate human movements and understand their physical limitations

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SDG des Nations Unies

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