基于实时csi的人机交互无线手势识别

A. Polo, M. Salucci, S. Verzura, Zhenkun Zhou, A. Massa
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引用次数: 1

摘要

研究和设计用于用户活动和手势识别的新方法和技术是人机交互领域的一个热门话题。基于计算机视觉的手势识别技术已经产生了令人印象深刻的结果,但它们涉及到用户的隐私问题,因此其他传感方法值得关注。在这项工作中,提出了一种基于被动电磁传感的新型机器学习方法,该方法利用商品Wi-Fi信号。这种方法已经在真实的房屋环境中进行了初步验证,分类准确率达到98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time CSI-Based Wireless Gesture Recognition for Human-Machine Interaction
The study and the design of novel methodologies and techniques for user's activity and gesture recognition is of great interest and a hot topic in human-computer interactions. Hand gesture recognition techniques based on computer-vision have yielded impressive results, but they involve users’ privacy concerns, therefore other sensing approaches are of interest. In this work, a novel machine learning methodology based on passive electromagnetic sensing that exploits commodity Wi-Fi signals is proposed. Such an approach has been preliminary validated in a real house environment with a classification accuracy of 98%.
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