Extending 3D body pose estimation for robotic-assistive therapies of
  autistic children
Laura Santos Bernardo Carvalho Catarina Barata José Santos-Victor
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.