Sport motional characteristic modeling is a hot research topic in human–computer interaction (HCI). Traditional sport motional characteristic based on single signal cannot achieve satisfactory performance. To solve this problem, we propose a multi-model feature fusion-based method for sport motional feature control. More specifically, we leverage Kinect sensor to acquire real-time video stream, where the main goal is to capture hand gesture as well as arm movements. HSV-based image segmentation is used for hand image patches extraction and recognition. To improve the effectiveness of sport motional characteristics control, we design audio-based HCI system to assist sport control. Our experiment is conducted on a quadruped robot platform with manipulator. Experimental results show the effectiveness of our proposed method.

运动运动特征建模是人机交互（HCI）研究的热点。传统的基于单信号的运动运动特性无法获得令人满意的性能。为了解决这个问题，我们提出了一种基于多模型特征融合的运动运动特征控制方法。更具体地说，我们利用Kinect传感器来获取实时视频流，其主要目标是捕获手势以及手臂动作。基于HSV的图像分割用于手图像补丁的提取和识别。为了提高运动运动特征控制的有效性，我们设计了基于音频的HCI系统来辅助运动控制。我们的实验是在带有机械手的四足机器人平台上进行的。实验结果表明了该方法的有效性。