点击次数：

论文类型：期刊论文

发表时间：2020-01-01

发表刊物：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录刊物：EI、SCIE

卷号：16

期号：1

页面范围：228-237

ISSN号：1551-3203

关键字：Deep network; device-free; gesture recognition; machine learning; wireless sensing

摘要：Device-free gesture recognition (DFGR) is a promising sensing technique, which can recognize a gesture by analyzing its influence on surrounding wireless signals. Most of the DFGR systems are designed based on machine learning. However, the recognition performance will drop dramatically when the testing condition is different with the training one. Inspired by the transferrable knowledge learning ability of humans, this paper develops a practical DFGR system based on metalearning to solve the aforementioned problem. Specifically, we design a deep network which could not only learn discriminative deep features, but also learn a transferrable similarity evaluation ability from the training set and apply the learned knowledge to the new testing conditions. Extensive experiments conducted by four users in two scenarios demonstrate that the proposed system could recognize new types of gestures, or gestures performed in new conditions, with an accuracy of more than 90, using very few number of new samples.