TY - GEN
T1 - A multi-sensor visual tracking system for behavior monitoring of at-risk children
AU - Sivalingam, Ravishankar
AU - Cherian, Anoop
AU - Fasching, Joshua
AU - Walczak, Nicholas
AU - Bird, Nathaniel
AU - Morellas, Vassilios
AU - Murphy, Barbara
AU - Cullen, Kathryn R
AU - Lim, Kelvin O
AU - Sapiro, Guillermo
AU - Papanikolopoulos, Nikolaos P
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Clinical studies confirm that mental illnesses such as autism, Obsessive Compulsive Disorder (OCD), etc. show behavioral abnormalities 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 by a clinician, which is a difficult and time intensive task. 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.
AB - Clinical studies confirm that mental illnesses such as autism, Obsessive Compulsive Disorder (OCD), etc. show behavioral abnormalities 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 by a clinician, which is a difficult and time intensive task. 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.
UR - http://www.scopus.com/inward/record.url?scp=84864453052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864453052&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2012.6225280
DO - 10.1109/ICRA.2012.6225280
M3 - Conference contribution
AN - SCOPUS:84864453052
SN - 9781467314039
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1345
EP - 1350
BT - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Y2 - 14 May 2012 through 18 May 2012
ER -