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Sports motional characteristics modeling by leveraging multi-modal image technique
Future Generation Computer Systems ( IF 6.2 ) Pub Date : 2021-01-27 , DOI: 10.1016/j.future.2021.01.031
Yanxia Li , Ke Zhao

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.



中文翻译:

利用多模态图像技术进行运动运动特征建模

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。

更新日期:2021-02-08
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