Sports motional characteristics modeling by leveraging multi-modal image technique

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The research on sport motional characteristic modeling in HCI field is of great significance, and a method based on multi-model feature fusion is proposed to control motion features. This method utilizes Kinect sensor to capture real-time video stream and utilizes HSV-based image segmentation to improve hand image recognition. Experimental results demonstrate the effectiveness of this method.