Temporal Coding in Spiking Neural Network for Gestures Recognition of A Partner Robot

Naoyuki Kubota; Yu Tomioka; Minoru Abe

doi:10.14864/softscis.2006.0.737.0

SCIS & ISIS 2006

Session ID : TH-E5-3

DOI https://doi.org/10.14864/softscis.2006.0.737.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

TH-E5 Brain and Perception

Temporal Coding in Spiking Neural Network for Gestures Recognition of A Partner Robot

Naoyuki Kubota, *Yu Tomioka, Minoru Abe

Author information

Keywords: Spiking Neural Networking, Gestures Recognition, Partner Robot

CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

This paper discusses the temporal coding in a gesture recognition method used for communication of a robot with a human. The proposed method is composed of a steady-state genetic algorithm for human hand detection, a spiking neural network for spatio-temporal pattern extraction of a human hand motion, and a self-organizing map for gesture clustering. In this paper, we discuss the effectiveness of the temporal coding of the spiking neural network through several experimental results.

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