Anthropometric clothing measurements from 3D body scans We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a predefined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on nonrigid iterative closest point algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a nonlinear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects, and the proposed pipeline provides mean absolute errors from 2.5 to 16.0 mm depending on the anthropometric measurement.

Step modeling and safe path planning for a lower limb exoskeleton The walking experience in environments with obstacles is a challenge for patients with lower limb pathology. A transparent exoskeleton is an interesting solution since it guarantees the performance of autonomous motion. In this paper, we present a new method to detect and model steps using point cloud data to find a feasible path for the exoskeleton to perform. We use a RGBD sensor to obtain depth information and perform a scene segmentation strategy. Next, we classify the different detected elements either as a floor, step or obstacle and then use a path planning method to find a collisionfree path. Experiments show that the system accomplished satisfactory results for the presented scenarios.