It is important to measure and analyze people behavior to design systems which interact with people. This article describes a portable people behavior measurement system using a three-dimensional LIDAR. In this system, an observer carries the system equipped with a three-dimensional Light Detection and Ranging (LIDAR) and follows persons to be measured while keeping them in the sensor view. The system estimates the sensor pose in a three-dimensional environmental map and tracks the target persons. It enables long-term and wide-area people behavior measurements which are hard for existing people tracking systems. As a field test, we recorded the behavior of professional caregivers attending elderly persons with dementia in a hospital. The preliminary analysis of the behavior reveals how the caregivers decide the attending position while checking the surrounding people and environment. Based on the analysis result, empirical rules to design the behavior of attendant robots are proposed.
This paper proposes a novel system to estimate and track the 3D poses of multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D pose of each person is computed by a central node which receives the single-view outcomes from each camera of the network. Each single-view outcome is computed by using a CNN for 2D pose estimation and extending the resulting skeletons to 3D by means of the sensor depth. The proposed system is marker-less, multiperson, independent of background and does not make any assumption on people appearance and initial pose. The system provides real-time outcomes, thus being perfectly suited for applications requiring user interaction. Experimental results show the effectiveness of this work with respect to a baseline multi-view approach in different scenarios. To foster research and applications based on this work, we released the source code in OpenPTrack, an open source project for RGB-D people tracking.
This paper discusses the new implementation of a strengthened introductory training course in Educational Robotics for pre-service and in-service learning support teachers. By means of a final written questionnaire we compare the results of the course in 2015 with this year course, when the number of hours were doubled. This year participants expressed a higher appreciation and a better attitude towards robotics. Teachers agreed on the conviction that robotics can enhance students' motivation to learning and that educational robotics sustains a new point of view on science for teachers. Regarding the implementation in class, approximately two third of the participants declare they had already an idea on how to integrate robotics in curricula. More specifically, participants named ASD (Autism Spectrum Disorders), ADHD (Attention deficit hyperactivity disorder), learning disabilities, mild mental retardation as aspects that can be effectively addressed by ER.
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