原文链接：Learning Gestures From WiFi: A Siamese Recurrent Convolutional Architecture | IEEE Journals & Magazine | IEEE Xplore
Methodology [原文图片，侵删]
System Design 上的改进
1）文章利用OpenWrt, 一款 lightweight Linux OS 作为IoT platform 的OS来收集WiFi CSI的数据， 相较于传统的Intel 5300 NIC tool，这个IoT platform可以被更大规模应用， 而且将subcarriers数目从30拓展到了114 （更多的subcarriers意味着更多的CSI info）
2）文章利用CSI phase data来做gestures learning, 因为认为phase比amplitude更sensitive
Siamese Recurrent Convolutional Network
Model
Inputs: two gesture stream samples
Loss function:
1) Pairwise loss: The pairwise loss guarantees that the distance between the same samples
is restricted within m − b while the margin of samples from different classes exceeds m + b.
2) MMD: 为了使得model具有更好的transferability， 可以有效处理来自不同domain的samples，引入multiple kernel variant of maximum mean discrepancies (MK-MMD)。MK-MMD bears the unique property for measuring two distributions of samples from different domains
Total loss：
Algorithm
利用one-shot learning 做gesture recognition
Experiments & Evaluations
