Extending 3D body pose estimation for robotic-assistive therapies of autistic children
CoRR(2024)
摘要
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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