扩展3D人体姿态估计以用于自闭症儿童的机器人辅助治疗
Extending 3D body pose estimation for robotic-assistive therapies of autistic children
AI总结：
针对自闭症儿童机器人辅助治疗中的姿态估计问题，通过改进现有3D人体建模方法并引入线性回归模型微调输入，开发出适用于儿童的3D姿态估计器，误差低于0.3m，在真实场景中帧估计成功率优于现有方法。
摘要：
机器人辅助疗法对自闭症儿童显示出了非常令人鼓舞的效果。准确估计儿童的姿态对于人机交互和治疗评估都至关重要。非侵入式方法是唯一可行的选择，因为这些儿童对触摸敏感。尽管深度相机已被广泛使用，但现有方法面临两大主要局限：（i）它们通常仅使用成人数据进行训练，无法正确估计儿童的姿态；（ii）它们在遮挡较多的场景中会失效。因此，我们的目标是通过改进一种现有的最先进的3D人体建模方法，并引入线性回归模型微调其输入之一，从而纠正儿童3D网格的姿态，开发出一种针对儿童的3D姿态估计器。在受控环境中，我们的方法误差低于0.3m，这对于此类应用而言是可以接受的，且低于当前最先进的方法。在真实世界环境中，所提出的模型表现与Kinect深度相机相似，并能在更多帧中成功估计3D人体姿态。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
