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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,9,6]]},"abstract":"<jats:p>Contactless RF-based sensing techniques are emerging as a viable means for building gesture recognition systems. While promising, existing RF-based gesture solutions have poor generalization ability when targeting new users, environments or device deployment. They also often require multiple pairs of transceivers and a large number of training samples for each target domain. These limitations either lead to poor cross-domain performance or incur a huge labor cost, hindering their practical adoption. This paper introduces Wi-Learner, a novel RF-based sensing solution that relies on just one pair of transceivers but can deliver accurate cross-domain gesture recognition using just one data sample per gesture for a target user, environment or device setup. Wi-Learner achieves this by first capturing the gesture-induced Doppler frequency shift (DFS) from noisy measurements using carefully designed signal processing schemes. It then employs a convolution neural network-based autoencoder to extract the low-dimensional features to be fed into a downstream model for gesture recognition. Wi-Learner introduces a novel meta-learner to \"teach\" the neural network to learn effectively from a small set of data points, allowing the base model to quickly adapt to a new domain using just one training sample. By so doing, we reduce the overhead of training data collection and allow a sensing system to adapt to the change of the deployed environment. We evaluate Wi-Learner by applying it to gesture recognition using the Widar 3.0 dataset. Extensive experiments demonstrate Wi-Learner is highly efficient and has a good generalization ability, by delivering an accuracy of 93.2% and 74.2% - 94.9% for in-domain and cross-domain using just one sample per gesture, respectively.<\/jats:p>","DOI":"10.1145\/3550318","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T14:54:27Z","timestamp":1662562467000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Wi-Learner"],"prefix":"10.1145","volume":"6","author":[{"given":"Chao","family":"Feng","sequence":"first","affiliation":[{"name":"Northwest University, Shaanxi International Joint Research Centre for the Battery-Free Internet of Things, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwest University, Shaanxi International Joint Research Centre for the Battery-Free Internet of Things, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yicheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Zhejiang University, School of Art and Archaeology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang University, School of Art and Archaeology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Li","sequence":"additional","affiliation":[{"name":"Northwest University, School of Information Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing, University of Leeds, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Northwest University, Shaanxi International Joint Research Centre for the Battery-Free Internet of Things, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430730"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS45731.2020.9181247"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3073969"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3427315"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430735"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191739"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131693"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00075"},{"key":"e_1_2_1_9_1","volume-title":"International Conference on Machine Learning. 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