In order to make gesture recognition applied in more fields

an approach based on the Leap Motion somatosensory devices real-time tracking technology is put forward to obtain 3 D space coordinate information of gesture

and extract angle information and relative coordinate information respectively

construct gesture feature data

establish gesture recognition model. After normalization of feature data

support vector machine（ SVM）classifier is used for training

modeling and classification

and gesture recognition is realized. The experimental results show that the method which uses angle data and coordinate data as gesture feature is feasible

the average recognition rate is respectively 96.6 % and 91.8 %.It can be concluded that using angle data as feature value has higher accuracy and robustness

and it can avoid the limitation of a simple eigenvalue

it has strong convincing.