A nonintrusive system for behavioral analysis of children using multiple RGB+depth sensors

Nicholas Walczak, Joshua Fasching, William Toczyski, Ravi Sivalingam, Nathaniel Bird, Kathryn Cullen, Vassilios Morellas, Barbara Murphy, Guillermo Sapiro, Nikolaos Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In developmental disorders such as autism and schizophrenia, observing behavioral precursors in very early childhood can allow for early intervention and can improve patient outcomes. While such precursors open the possibility of broad and large-scale screening, until now they have been identified only through experts' painstaking examinations and their manual annotations of limited, unprocessed video footage. Here we introduce a system to automate and assist in such procedures. Employing multiple inexpensive real-time rgb+depth (rgb+d) sensors recording from multiple viewpoints, our non-invasive systemnow installed at the Shirley G. Moore Lab School, a research preschool-is being developed to monitor and reconstruct the play and interactions of preschoolers. The system's role is to help in assessing the growing volumes of its on-site recordings and to provide the data needed to uncover additional neuromotor behavioral markers via techniques such as data mining.

Original languageEnglish (US)
Pages (from-to)217-222
Number of pages6
JournalProceedings of IEEE Workshop on Applications of Computer Vision
StatePublished - May 11 2012
Event2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012 - Breckenridge, CO, United States
Duration: Jan 9 2012Jan 11 2012


Dive into the research topics of 'A nonintrusive system for behavioral analysis of children using multiple RGB+depth sensors'. Together they form a unique fingerprint.

Cite this