DocumentCode :
186275
Title :
A model for biological motion detection based on motor prediction in the dorsal premotor area
Author :
Kawai, Yusuke ; Asada, Minoru ; Nagai, Yukie
Author_Institution :
Grad. Sch. of Eng., Osaka Univ., Suita, Japan
fYear :
2014
fDate :
13-16 Oct. 2014
Firstpage :
249
Lastpage :
255
Abstract :
Recent findings regarding dorsal premotor area (PMd) activation during observation of smooth biological movements suggest that this motor-related area detects biological motions. We hypothesize that a neural network in the PMd acquires an invariance of self-induced motor commands for smooth movements and interprets the observed biological motions as ones satisfying the invariance in self-movements. To verify our hypothesis, we developed a recurrent neural network (RNN) to be trained with smooth motor movements, and examined how the RNN acquires biological invariance. The results showed that predictive learning of the RNN contributed to invariance acquisition, which enabled it to detect biological motions. Our findings agree with the fact that the PMd originally functions as a motor predictor. Moreover, this RNN could judge the ankle and wrist trajectories of a walking human as biological regardless of the subject´s sex and emotional state.
Keywords :
gait analysis; learning (artificial intelligence); neurophysiology; recurrent neural nets; PMd activation; RNN; ankle trajectories; biological invariance; biological motion detection; biological movements; dorsal premotor area activation; emotional state; invariance acquisition; motor prediction; motor-related area; recurrent neural network; self-induced motor commands; self-movements; smooth motor movements; walking human; wrist trajectories; Acceleration; Biological information theory; Biological neural networks; Biological system modeling; Predictive models; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2014 Joint IEEE International Conferences on
Conference_Location :
Genoa
Type :
conf
DOI :
10.1109/DEVLRN.2014.6982989
Filename :
6982989
Link To Document :
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