Lin, J.F.-S. and Kulic, D. (2014) Online Segmentation of Human Motion for Automated Rehabilitation Exercise Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22, 168-180.
https://doi.org/10.1109/TNSRE.2013.2259640

has been cited by the following article:

• TITLE: Human Skeleton Detection, Modeling and Gesture Imitation Learning for a Social Purpose

  AUTHORS: Linda Nanan Vallée, Sao Mai Nguyen, Christophe Lohr, Ioannis Kanellos, Olivier Asseu

  KEYWORDS: Imitation Learning, Artificial Intelligence, Gesture Recognition, Autism Spectrum Disorders (ASD), Gaussian Mixture Model (GMM)

  JOURNAL NAME: Engineering, Vol.12 No.2, February 27, 2020

  ABSTRACT: Gesture recognition is topical in computer science and aims at interpreting human gestures via mathematical algorithms. Among the numerous applications are physical rehabilitation and imitation games. In this work, we suggest performing human gesture recognition within the context of a serious imitation game, which would aim at improving social interactions with teenagers with autism spectrum disorders. We use an artificial intelligence algorithm to detect the skeleton of the participant, then model the human pose space and describe an imitation learning method using a Gaussian Mixture Model in the Riemannian manifold.