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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2023,3,27]]},"abstract":"<jats:p>Radio-Frequency (RF) based human activity recognition (HAR) enables many attractive applications such as smart home, health monitoring, and virtual reality (VR). Among multiple RF sensors, mmWave radar is emerging as a new trend due to its fine-grained sensing capability. However, laborious data collection and labeling processes are required when employing a radar-based sensing system in a new environment. To this end, we propose RF-CM, a general cross-modal human activity recognition framework. The key enabler is to leverage the knowledge learned from a massive WiFi dataset to build a radar-based HAR system with limited radar samples. It can significantly reduce the overhead of training data collection. In addition, RF-CM can work well regardless of the deployment setups of WiFi and mmWave radar, such as performing environments, users' characteristics, and device deployment. RF-CM achieves this by first capturing the activity-related variation patterns through data processing schemes. It then employs a convolution neural network-based feature extraction module to extract the high-dimensional features to be fed into the activity recognition module. Finally, RF-CM takes the generalization knowledge from WiFi networks as guide labels to supervise the training of the radar model, thus enabling a few-shot radar-based HAR system. We evaluate RF-CM by applying it to two HAR applications, fine-grained American sign language recognition (WiFi-cross-radar) and coarse-grained gesture recognition (WiFi-cross-RFID). The accuracy improvement of over 10% in both applications demonstrates the effectiveness of RF-CM. This cross-modal ability allows RF-CM to support more cross-modal applications.<\/jats:p>","DOI":"10.1145\/3580859","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T14:57:51Z","timestamp":1680015471000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["RF-CM"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6271-0388","authenticated-orcid":false,"given":"Xuan","family":"Wang","sequence":"first","affiliation":[{"name":"Northwest University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4313-9809","authenticated-orcid":false,"given":"Tong","family":"Liu","sequence":"additional","affiliation":[{"name":"Northwest University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7322-2320","authenticated-orcid":false,"given":"Chao","family":"Feng","sequence":"additional","affiliation":[{"name":"Northwest University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-6922","authenticated-orcid":false,"given":"Dingyi","family":"Fang","sequence":"additional","affiliation":[{"name":"Northwest University, Shaanxi International Joint Research Centre for the Battery-Free Internet of Things, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-6806","authenticated-orcid":false,"given":"Xiaojiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Northwest University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445138"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.12.003"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494994"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3432224"},{"key":"e_1_2_1_5_1","volume-title":"Reviewing continual learning from the perspective of human-level intelligence. arXiv preprint arXiv:2111.11964","author":"Chang Yifan","year":"2021","unstructured":"Yifan Chang, Wenbo Li, Jian Peng, Bo Tang, Yu Kang, Yinjie Lei, Yuanmiao Gui, Qing Zhu, Yu Liu, and Haifeng Li. 2021. 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