End-to-end Learning of Body Weight Prediction from Point Clouds with Basis Point Sets
Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich
Universität zu Lübeck, Institut für Medizinische Informatik
The body weight of a patient is an important parameter in many clinical settings, e.g. when it comes to drug dosing or anesthesia. However, assessing the weight through direct interaction with the patient (anamnesis, weighing) is often infeasible. Therefore, there is a need for the weight to be estimated in a contactless way from visual inputs. This work addresses weight prediction of patients lying in bed from 3D point cloud data by means of deep learning techniques. Contrary to prior work in this field, we propose to learn the task in an end-to-end fashion without relying on hand-crafted features. For this purpose, we adopt the concept of basis point sets to encode the input point cloud into a lowdimensional feature vector. This vector is passed to a neural network, which is trained for weight regression. As the originally proposed construction of the basis point set is not ideal for our problem, we develop a novel sampling scheme, which exploits prior knowledge about the distribution of input points. We evaluate our approach on a lying pose dataset (SLP) and achieve weight estimates with a mean absolute error of 4.2 kg and a mean relative error of 6.4 % compared to 4.8 kg and 7.0 % obtained with a basic PointNet.