@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\}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-11012-3\_15",
language = "English",
isbn = "9783030110116",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "190--197",
editor = "Stefan Roth and Laura Leal-Taix{\'e}",
booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",
}
