关键词: 动态手势识别, Leap Motion传感器, 特征提取, 长短期记忆网络（LSTM）

Abstract: Dynamic gesture recognition is one of the most important topics in human-computer interaction. Compared with static gestures, the change of dynamic gestures is more complicated. The full extraction and description of dynamic gesture features is the key to accurately identify dynamic gestures. In order to solve the problem of insufficient description of dynamic gesture features and low recognition efficiency, this paper proposes a feature sequence that can fully describe dynamic gestures and realize effective description of dynamic gestures. High-precision Leap Motion sensor is used to acquire hand parameters diametrically. A sequence feature including fingertips angles, fingertips distances, fingertips elevations and palm speed is proposed. In addition, the sequence is combined with Long Short-Term Memory（LSTM） network model for dynamic gesture recognition. And by analyzing the experimental results on the dynamic gesture dataset, a dataset containing 16 dynamic gestures is collected by Leap Motion, which contains complex finger changes and hand movements. The dataset is named LM-16. The experimental results show that the recognition accuracy of this method for the LM-16 dataset is 98.50%. Furthermore, experiments are carried out by using other feature sequences to compare the influence of feature sequences on dynamic gestures recognition.

Key words: dynamic gesture recognition, Leap Motion sensor, feature extraction, Long Short-Term Memory（LSTM）