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.

我们开发了一种适用于儿童的三维姿势估计器，通过改进现有的三维身体建模方法并结合线性回归模型来纠正儿童的三维网格姿势，从而在控制环境和真实环境中成功地估计儿童的三维身体姿势。