Extending 3D body pose estimation for robotic-assistive therapies of autistic children

• URL: http://arxiv.org/abs/2402.08006v1
• Date: Mon, 12 Feb 2024 19:11:03 GMT
• Title: Extending 3D body pose estimation for robotic-assistive therapies of autistic children
• Authors: Laura Santos, Bernardo Carvalho, Catarina Barata, Jos\'e Santos-Victor
• Abstract summary: We develop a 3D pose estimator for children with Autism. Our method has an error below $0.3m$, which is considered acceptable for this kind of application. In real-world settings, the proposed model performs similarly to a Kinect depth camera.
• Score: 4.751886527142779
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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