Teaching RF to Sense without RF Training Measurements
Teaching RF to Sense without RF Training Measurements
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DOI：
10.1145/3432224
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发表时间：
2020-12
影响因子：
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通讯作者：
H. Cai;Belal Korany;Chitra R. Karanam;Yasamin Mostofi
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文献类型：
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作者：
H. Cai;Belal Korany;Chitra R. Karanam;Yasamin Mostofi
在本文中，我们提出了一种新颖的，可推广的，可扩展的想法，消除了收集射频（RF）测量的需要，当训练人体运动相关活动的RF传感系统。现有的基于学习的RF感测系统需要收集大量的RF训练数据，这在很大程度上取决于特定的感测设置/所涉及的活动。因此，当设置/活动改变时，需要收集新的数据，这显著地限制了RF感测系统的实际部署。另一方面，近年来，涉及各种人类活动/运动的大量在线视频不断增长。在本文中，我们建议将这些已经可用的在线视频转换为即时模拟RF数据，用于在任何给定的设置中训练任何基于人体运动的RF传感系统。为了验证我们提出的框架，我们进行了一个案例研究的健身房活动分类，CSI幅度测量的三个WiFi链路被用来分类一个人的活动，从10个不同的体育锻炼。我们利用YouTube健身房活动视频，并通过模拟如果视频中的人在收发器附近进行活动时会测量到的WiFi信号将其转换为RF。然后，我们在模拟数据上训练分类器，并用10个受试者在3个区域进行活动的真实的WiFi数据对其进行广泛测试。我们的系统实现了86%的分类准确率的活动周期，每个包含平均5.1运动重复，和81%的个人重复的练习。这表明我们的方法可以从已经可用的视频中生成可靠的RF训练数据，并且可以在没有任何真实的RF测量的情况下成功地训练RF感测系统。拟议的管道还可以用于培训之外，以及用于RF传感系统的分析和设计，而不需要大量的RF数据收集。
In this paper, we propose a novel, generalizable, and scalable idea that eliminates the need for collecting Radio Frequency (RF) measurements, when training RF sensing systems for human-motion-related activities. Existing learning-based RF sensing systems require collecting massive RF training data, which depends heavily on the particular sensing setup/involved activities. Thus, new data needs to be collected when the setup/activities change, significantly limiting the practical deployment of RF sensing systems. On the other hand, recent years have seen a growing, massive number of online videos involving various human activities/motions. In this paper, we propose to translate such already-available online videos to instant simulated RF data for training any human-motion-based RF sensing system, in any given setup. To validate our proposed framework, we conduct a case study of gym activity classification, where CSI magnitude measurements of three WiFi links are used to classify a person's activity from 10 different physical exercises. We utilize YouTube gym activity videos and translate them to RF by simulating the WiFi signals that would have been measured if the person in the video was performing the activity near the transceivers. We then train a classifier on the simulated data, and extensively test it with real WiFi data of 10 subjects performing the activities in 3 areas. Our system achieves a classification accuracy of 86% on activity periods, each containing an average of 5.1 exercise repetitions, and 81% on individual repetitions of the exercises. This demonstrates that our approach can generate reliable RF training data from already-available videos, and can successfully train an RF sensing system without any real RF measurements. The proposed pipeline can also be used beyond training and for analysis and design of RF sensing systems, without the need for massive RF data collection.