Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to
assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately.
Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the
performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands,
is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not
considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological
network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components,
corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian
rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features,
large amount of samples can be used in learning efficiently.