PREDICTION OF GROUND REACTION FORCES AND MOMENTS VIA SUPERVISED LEARNING IS INDEPENDENT OF PARTICIPANT SEX, HEIGHT AND MASS

Accurate multidimensional ground reaction forces and moments (GRF/Ms) can be predicted from marker-based motion capture using Partial Least Squares (PLS) supervised learning. In this study, the correlations between known and predicted GRF/Ms are compared depending on whether the PLS model is trained using the discrete inputs of sex, height and mass. All three variables were found to be accounted for in the marker trajectory data, which serves to simplify data capture requirements and importantly, indicates that prediction of GRF/Ms can be achieved without pre-existing knowledge of such participant specific inputs. This multidisciplinary research approach significantly advances machine representation of real-world physical attributes with direct application to sports biomechanics

Manipulation of Polymorphic Objects Using Two Robotic Arms through CNN Networks

This article presents an interaction system for two 5 DOF (Degrees of Freedom) manipulators with 3-finger grippers, which will be used to grab and displace up to 10 polymorphic objects shaped as pentominoes, inside a VRML (Virtual Reality Modeling Language) environment, by performing element detection and classification using an R-CNN (Region Proposal Convolutional Neural Network), and point detection and gripping orientation using a DAG-CNN (Directed Acyclic Graph-Convolutional Neural Network). It was analyzed the feasibility or not of a grasp is determined depending on how the geometry of an element fits the free space between the gripper fingers. A database was created to be used as training data with each of the grasp positions for the polyshapes, so the network training can be focused on finding the desired grasp positions, enabling any other grasp found to be considered a feasible grasp, and eliminating the need to find additional better grasp points, changing the shape, inclination and angle of rotation. Under varying test conditions, the test successfully achieved gripping of each object with one manipulator and passing it to the second manipulator as part of the grouping process, in the opposite end of the work area, using an R-CNN and a DAG-CNN, with an accuracy of 95.5% and 98.8%, respectively, and performing a geometric analysis of the objects to determine the displacement and rotation required by the gripper for each individual grip