TY - JOUR
T1 - Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning
AU - Hong, Weizhe
AU - Kennedy, Ann
AU - Burgos-Artizzu, Xavier P.
AU - Zelikowsky, Moriel
AU - Navonne, Santiago G.
AU - Perona, Pietro
AU - Anderson, David J.
PY - 2015/9/22
Y1 - 2015/9/22
N2 - A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animalmodels has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.
AB - A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animalmodels has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.
KW - Behavioral tracking
KW - Depth sensing
KW - Machine vision
KW - Social behavior
KW - Supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=84942914975&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84942914975&partnerID=8YFLogxK
U2 - 10.1073/pnas.1515982112
DO - 10.1073/pnas.1515982112
M3 - Article
C2 - 26354123
AN - SCOPUS:84942914975
SN - 0027-8424
VL - 112
SP - E5351-E5360
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 38
ER -