Title :
Human gesture recognition for robot partners by spiking neural network and classification learning
Author :
Botzheim, Janos ; Obo, Takenori ; Kubota, Naoyuki
Author_Institution :
Grad. Sch. of Syst. Design, Tokyo Metropolitan Univ., Tokyo, Japan
Abstract :
Recently, the rate of elderly people rises in the super-aging society. Human-friendly robots can be used to support the mental and physical care for elderly people and to assist the care of caregivers to elderly people. Robotic conversation can activate the brain of such elderly people and improve their concentration and memory abilities. However, it is difficult for a robot to converse appropriately with a person even if many contents of the conversation are designed in advance because the performance of voice recognition is not enough in the daily conversation. Recognition of human gestures is also important in order to perform smooth communication. This paper deals with human gestures recognition using spiking neural network and classification learning. The proposed method is able to handle the cultural differences in the human communication.
Keywords :
gesture recognition; handicapped aids; image classification; neural nets; robot vision; classification learning; elderly people; human communication; human friendly robots; human gesture recognition; mental care; physical care; robot partners; robotic conversation; smooth communication; spiking neural network; super aging society; voice recognition;
Conference_Titel :
Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on
Conference_Location :
Kobe
Print_ISBN :
978-1-4673-2742-8
DOI :
10.1109/SCIS-ISIS.2012.6505305