ICRA 2012 Paper Abstract


Paper TuC210.4

Sivalingam, Ravishankar (University of Minnesota), Cherian, Anoop (U. of Minnesota), Fasching, Joshua (University of Minnesota), Walczak, Nicholas (University of Minnesota), Morellas, Vassilios (U. of Minnesota), Papanikolopoulos, Nikos (University of Minnesota), Lim, Kelvin (Psychiatry), Sapiro, Guillermo (University of Minnesota), Murphy, Barbara (University of Minnesota), Bird, Nathaniel (Ohio Northern University), Cullen, Kathryn (UMN)

A Multi-Sensor Visual Tracking System for Behavior Monitoring of At-Risk Children

Scheduled for presentation during the Interactive Session "Interactive Session TuC-2" (TuC210), Tuesday, May 15, 2012, 15:00−15:30, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on August 21, 2018

Keywords Medical Robots and Systems, Automation in Life Sciences: Biotechnology, Pharmaceutical and Health Care, Surveillance Systems


Clinical studies confirm that mental illnesses such as autism, Obsessive Compulsive Disorder (OCD), etc. show behavioral disorders even at very young ages; the early diagnosis of which can help steer effective treatments. Most often, the behavior of such at-risk children deviate in very subtle ways from that of a normal child; correct diagnosis of which requires prolonged and continuous monitoring of their activities for the diagnosis, which is a cumbersome task for today’s standards. As a result, the development of automation tools for assisting in such monitoring activities will be an important step towards effective utilization of the diagnostic resources. In this paper, we approach the problem from a computer vision standpoint, and propose a novel system for the automatic monitoring of the behavior of children in their natural environment through the deployment of multiple non-invasive sensors (cameras and depth sensors). We provide details of our system, together with algorithms for the robust tracking of the activities of the children. Our experiments, conducted in the Shirley G. Moore Laboratory School, demonstrate the effectiveness of our methodology.



Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2018 PaperCept, Inc.
Page generated 2018-08-21  18:12:03 PST  Terms of use