TY - GEN
T1 - Development of a cognition system for analyzing rat's behaviors
AU - Shi, Qing
AU - Miyagishima, Shunsyuke
AU - Fumino, Shogo
AU - Konno, Shinichiro
AU - Ishii, Hiroyuki
AU - Takanishi, Atsuo
PY - 2010
Y1 - 2010
N2 - The interaction experiment, between a robot and a rat, will benefit significantly when the rat's actions can be recognized automatically in real time. Regarding quantitative behavior analysis, the number and duration of a rat's actions should be measured efficiently and accurately. Therefore, aiming at the above-mentioned objectives, a novel cognition system capable of detecting rats' actions has been proposed in this paper. The main function of this cognition system lies on the real-time recognition and offline analysis of rats' behaviors. Basic image processing algorithm as Labeling and Contour Finding were employed to extract feature parameters (body length, body area, body radius, rotational angle, and ellipticity) of rat's actions. These parameters are integrated as the input feature vector of NN (Neural Network) and SVM (Support Vector Machine) training system respectively. Preliminary experiments reveal that the grooming, rotating and rearing actions could be recognized with extremely high rate (more than 90%) by both NN and SVM. Compared to NN, SVM provides better recognition rate and less computational cost.
AB - The interaction experiment, between a robot and a rat, will benefit significantly when the rat's actions can be recognized automatically in real time. Regarding quantitative behavior analysis, the number and duration of a rat's actions should be measured efficiently and accurately. Therefore, aiming at the above-mentioned objectives, a novel cognition system capable of detecting rats' actions has been proposed in this paper. The main function of this cognition system lies on the real-time recognition and offline analysis of rats' behaviors. Basic image processing algorithm as Labeling and Contour Finding were employed to extract feature parameters (body length, body area, body radius, rotational angle, and ellipticity) of rat's actions. These parameters are integrated as the input feature vector of NN (Neural Network) and SVM (Support Vector Machine) training system respectively. Preliminary experiments reveal that the grooming, rotating and rearing actions could be recognized with extremely high rate (more than 90%) by both NN and SVM. Compared to NN, SVM provides better recognition rate and less computational cost.
UR - https://www.scopus.com/pages/publications/79952907982
U2 - 10.1109/ROBIO.2010.5723534
DO - 10.1109/ROBIO.2010.5723534
M3 - Conference contribution
AN - SCOPUS:79952907982
SN - 9781424493173
T3 - 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
SP - 1399
EP - 1404
BT - 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
PB - IEEE Computer Society
T2 - 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
Y2 - 14 December 2010 through 18 December 2010
ER -