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.
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