Extending 3D body pose estimation for robotic-assistive therapies of autistic children
<|reference_start|>Extending 3D body pose estimation for robotic-assistive therapies of autistic children: Robotic-assistive therapy has demonstrated very encouraging results for children with Autism. Accurate estimation of the child's pose is essential both for human-robot interaction and for therapy assessment purposes. Non-intrusive methods are the sole viable option since these children are sensitive to touch.   While depth cameras have been used extensively, existing methods face two major limitations: (i) they are usually trained with adult-only data and do not correctly estimate a child's pose, and (ii) they fail in scenarios with a high number of occlusions. Therefore, our goal was to develop a 3D pose estimator for children, by adapting an existing state-of-the-art 3D body modelling method and incorporating a linear regression model to fine-tune one of its inputs, thereby correcting the pose of children's 3D meshes.   In controlled settings, our method has an error below $0.3m$, which is considered acceptable for this kind of application and lower than current state-of-the-art methods. In real-world settings, the proposed model performs similarly to a Kinect depth camera and manages to successfully estimate the 3D body poses in a much higher number of frames.<|reference_end|>
@article{santos2024extending,
    title={Extending 3D body pose estimation for robotic-assistive therapies of
  autistic children},
    author={Laura Santos, Bernardo Carvalho, Catarina Barata, Jos'e Santos-Victor},
    journal={arXiv preprint arXiv:2402.08006},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08006},
    primaryClass={cs.RO cs.CV cs.HC}
}