{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:55:45Z","timestamp":1760151345603,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172003"],"award-info":[{"award-number":["62172003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gesture recognition plays an important role in smart homes, such as human\u2013computer interaction, identity authentication, etc. Most of the existing WiFi signal-based approaches exploit a large number of channel state information (CSI) datasets to train a gestures classification model; however, these models require a large number of human participants to train, and are not robust to the recognition environment. To address this problem, we propose a WiFi signal-based gesture recognition system with matched averaging federated learning (WiMA). Since there are differences in the distribution of WiFi signal changes caused by the same gesture in different environments, the traditional federated parameter average algorithm seriously affects the recognition accuracy of the model. In WiMA, we exploit the neuron arrangement invariance of neural networks in parameter aggregation, which can improve the robustness of the gesture recognition model with heterogeneous CSI data of different training environments. We carried out experiments with seven participant users in a distributed gesture recognition environment. Experimental results show that the average accuracy of our proposed system is up to 90.4%, which is very close to the accuracy of state-of-the-art approaches with centralized training models.<\/jats:p>","DOI":"10.3390\/s22062349","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"2349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["WiFi Signal-Based Gesture Recognition Using Federated Parameter-Matched Aggregation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1324-3960","authenticated-orcid":false,"given":"Weidong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China"},{"name":"Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet, Maanshan 243032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zexing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China"},{"name":"Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet, Maanshan 243032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7912-8757","authenticated-orcid":false,"given":"Xuangou","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China"},{"name":"Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet, Maanshan 243032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Oudah, M., Al-Naji, A., and Chahl, J.S. (2020). Hand Gesture Recognition Based on Computer Vision: A Review of Techniques. J. Imaging, 6.","DOI":"10.3390\/jimaging6080073"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sinha, K., Kumari, R., Priya, A., and Paul, P. (2019). A Computer Vision-Based Gesture Recognition Using Hidden Markov Model. Innovations in Soft Computing and Information Technology, Springer.","DOI":"10.1007\/978-981-13-3185-5_6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5569","DOI":"10.1007\/s12652-020-01913-3","article-title":"A novel muscle-computer interface for hand gesture recognition using depth vision","volume":"11","author":"Zhou","year":"2020","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1007\/s11036-020-01590-8","article-title":"Gesture Recognition Through sEMG with Wearable Device Based on Deep Learning","volume":"25","author":"Shen","year":"2020","journal-title":"Mob. Netw. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11101","DOI":"10.1007\/s00500-021-05855-6","article-title":"Real-time sign language framework based on wearable device: Analysis of MSL, DataGlove, and gesture recognition","volume":"25","author":"Ahmed","year":"2021","journal-title":"Soft Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14703","DOI":"10.1109\/JSEN.2020.3011825","article-title":"Hand Gesture Recognition by a MMG-Based Wearable Device","volume":"20","author":"Liu","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Al-qaness, M.A.A., and Li, F. (2016). WiGeR: WiFi-based gesture recognition system. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5060092"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TVT.2018.2878754","article-title":"TW-See: Human activity recognition through the wall with commodity Wi-Fi devices","volume":"68","author":"Wu","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zheng, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., and Yang, Z. (2021). Widar3. 0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2021.3105387"},{"key":"ref_10","unstructured":"McMahan, H.B., Moore, E., Ramage, D., and Arcas, B.A.Y. (2016). Federated Learning of Deep Networks using Model Averaging. arXiv."},{"key":"ref_11","unstructured":"Pillutla, K., Laguel, Y., Malick, J., and Harchaoui, Z. (2021). Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TCE.2020.3021398","article-title":"A WiFi-based Smart Home Fall Detection System using Recurrent Neural Network","volume":"66","author":"Ding","year":"2020","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3161183","article-title":"FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices","volume":"1","author":"Palipana","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Venkatnarayan, R.H., Page, G., and Shahzad, M. (2018, January 10\u201315). Multi-user gesture recognition using WiFi. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, Munich, Germany.","DOI":"10.1145\/3210240.3210335"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1038\/s41467-020-15086-2","article-title":"Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks","volume":"11","author":"Golestani","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shang, J., and Wu, J. (2017, January 25). A robust sign language recognition system with multiple Wi-Fi devices. Proceedings of the Workshop on Mobility in the Evolving Internet Architecture, Los Angeles, CA, USA.","DOI":"10.1145\/3097620.3097624"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, M., and Cao, Z. (2020, January 6\u20139). WiHF: Enable User Identified Gesture Recognition with WiFi. Proceedings of the IEEE INFOCOM 2020\u2014IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155539"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gu, Y., and Li, J. (2021, January 26\u201328). A Novel WiFi Gesture Recognition Method Based on CNN-LSTM and Channel Attention. Proceedings of the 2021 3rd International Conference on Advanced Information Science and System (AISS 2021), Sanya, China.","DOI":"10.1145\/3503047.3503148"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"205","DOI":"10.23919\/JCC.2021.03.016","article-title":"WiFi CSI gesture recognition based on parallel LSTM-FCN deep space-time neural network","volume":"18","author":"Tang","year":"2021","journal-title":"China Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","article-title":"Adaptive Federated Learning in Resource Constrained Edge Computing Systems","volume":"37","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_21","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y. (2020). Federated learning with matched averaging. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shukla, S., and Srivastava, N. (2021, January 21\u201323). Federated matched averaging with information-gain based parameter sampling. Proceedings of the First International Conference on AI-ML-Systems, Bangalore, India.","DOI":"10.1145\/3486001.3486225"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, X., Li, S., Zhang, D., Xiong, J., Wang, Y., and Mei, H. (2016, January 12\u201316). Dynamic-music: Accurate device-free indoor localization. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971665"},{"key":"ref_24","unstructured":"Qian, K., Wu, C., Yang, Z., Liu, Y., and Jamieson, K. (2017, January 10\u201314). Widar: Decimeter-level passive tracking via velocity monitoring with commodity Wi-Fi. Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Chennai, India."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., and Yang, Z. (2019, January 17\u201321). Zero-effort cross-domain gesture recognition with Wi-Fi. Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, Seoul, Korea.","DOI":"10.1145\/3307334.3326081"},{"key":"ref_26","unstructured":"Jiang, W., Miao, C., Ma, F., Yao, S., Wang, Y., Yuan, Y., Xue, H., Song, C., Ma, X., and Koutsonikolas, D. (November, January 29). Towards environment independent device free human activity recognition. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India."},{"key":"ref_27","unstructured":"Zhang, J., Tang, Z., Li, M., Fang, D., Nurmi, P., and Wang, Z. (November, January 29). CrossSense: Towards cross-site and large-scale WiFi sensing. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qian, K., Wu, C., Zhou, Z., Zheng, Y., Yang, Z., and Liu, Y. (2017, January 6\u201311). Inferring motion direction using commodity wi-fi for interactive exergames. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA.","DOI":"10.1145\/3025453.3025678"},{"key":"ref_29","unstructured":"Arivazhagan, M.G., Aggarwal, V., Singh, A.K., and Choudhary, S. (2019). Federated learning with personalization layers. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2349\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:38:46Z","timestamp":1760135926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2349"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062349"],"URL":"https:\/\/doi.org\/10.3390\/s22062349","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,3,18]]}}}