In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.

中文翻译：

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基于多特征隐马尔可夫模型的动态手势融合识别方法。

本文提出了一种基于多重特征和隐马尔可夫模型（HMM）的融合方法，用于识别机器人遥操作中与操作员指令相对应的动态手势。首先，需要根据连续移动的手的速度从连续获得的数据中分离出有效的动态手势。其次，引入用于动态手势表达的特征集，其包括四种特征：手掌姿势，弯曲角度，手指的张开角度和手势轨迹。最后，建立了基于这些特征的HMM分类器，并提出了融合四种特征概率的加权计算模型。通过识别通过跳跃运动（LM）获取的动态手势对提出的方法进行了评估，该方法的识别率约为90。