A Method for Digital Representation of Human Movements

In this work we present a method to produce a model of human motion based on an expansion in functions series. The model is thought to reproduce the learned movements generalizing them to different conditions. We will show, with an example, how the proposed method is capable to produce the model from a reduced set of examples preserving the relevant features of the demonstrations while guaranteeing constraints at boundaries

A 6-DOF haptic manipulation system to verify assembly procedures on CAD models

During the design phase of products and before going into production, it is necessary to verify the presence of mechanical plays, tolerances, and encumbrances on production mockups. This work introduces a multi-modal system that allows verifying assembly procedures of products in Virtual Reality starting directly from CAD models. Thus leveraging the costs and speeding up the assessment phase in product design. For this purpose, the design of a novel 6-DOF Haptic device is presented. The achieved performance of the system has been validated in a demonstration scenario employing state-of-the-art volumetric rendering of interaction forces together with a stereoscopic visualization setup

Cognitive Human-Computer Communication by means of Haptic Interfaces

The present work analyzes the effects on learning and cognitive processes when augmenting user feedback by means of the force feedback devices. Different haptic devices have been employed to test three different cognitive processes: feature and shape recognition, trajectory learning and co-writing. In order to assess the effectiveness of the haptic feedback we compared in the three test cases the force feedback with alternate methods of displaying feedback (acoustic and visual stimuli), as a result we found that information provided by haptic interfaces complements the one which is achieved by vision and audio and, if integrated helps to improve specific kind of performances. After reviewing the three different test cases and the achieved result we will try to provide some indications on how to exploit haptic feedback to improve gesture performance in skill training application

Guided latent space regression for human motion generation

In the present work, we describe a mathematical model to generate human-like motion trajectories in space. We use linear regression in a latent space to find the model parameters from a set of demonstration examples. The learning procedure requires a relevant set of similar examples. The apprehended models encode both the typical shapes of motion and their variability towards specific boundary conditions (BC). We will show the added value of encoding both properties in a unique model and we apply this ability to common problems of error compensation and target tracking. The models allow us to describe human motion using expansion-function series (EFS), thus avoiding typical stability issues that arise in the use of differential equation models. To cope with variable scenarios, we show two specific algorithms that morph and adapt the evolution trajectory. In analogy to splines, the EFS preserve an analytical structure on which we develop the optimisation steps. In such a way, we managed to combine multiple single segments into complex motions that preserve continuity and may simultaneously optimise other criteria. In the present work, after having analysed similar tools, we present the basic model and its features. Then we develop a robust tool to gather the model from examples, and to achieve real-time trajectory adaptation. The achieved results will be analysed through an experimental analysis on data collected in a ball catching experiment