A hand shape instruction recognition and learning system using growing SOM with asymmetric neighborhood function

For Human–Machine Interaction systems, it is a convenient method to send user׳s instructions to robots, TV sets, and other electronic equipments by showing different shapes of a hand of user. In our previous works, we proposed to use improved Kohonen׳s Self-Organizing Maps (SOMs), i.e., Transient-SOM (T-SOM) and Parameterless Growing SOM (PL-G-SOM) to recognize different patterns of hand shapes given by different bendings of five fingers of a hand. Recently, an asymmetric neighborhood function was proposed and introduced into the conventional SOM to improve the learning performance by Aoki and Aoyagi. In this paper, we propose to employ their asymmetric neighborhood function into Growing SOM (GSOM), which is an improved SOM to deal with additional online learning for input data. Furthermore, the improved GSOM is applied to a hand shape recognition and instruction learning system, and the results of experiments with eight kinds of instructions showed the effectiveness of the proposed system.