{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-survey-of-human-activity-recognition-using","title":"A Survey of Human Activity Recognition Using WiFi CSI","arxiv_id":"1708.07129","date":"2017-08-23","proceeding":null,"authors":["Siamak Yousefi","Hirokazu Narui","Sankalp Dayal","Stefano Ermon","Shahrokh Valaee"],"abstract":"In this article, we present a survey of recent advances in passive human\nbehaviour recognition in indoor areas using the channel state information (CSI)\nof commercial WiFi systems. Movement of human body causes a change in the\nwireless signal reflections, which results in variations in the CSI. By\nanalyzing the data streams of CSIs for different activities and comparing them\nagainst stored models, human behaviour can be recognized. This is done by\nextracting features from CSI data streams and using machine learning techniques\nto build models and classifiers. The techniques from the literature that are\npresented herein have great performances, however, instead of the machine\nlearning techniques employed in these works, we propose to use deep learning\ntechniques such as long-short term memory (LSTM) recurrent neural network\n(RNN), and show the improved performance. We also discuss about different\nchallenges such as environment change, frame rate selection, and multi-user\nscenario, and suggest possible directions for future work.","url_abs":"http://arxiv.org/abs/1708.07129v1","url_pdf":"http://arxiv.org/pdf/1708.07129v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-survey-of-human-activity-recognition-using","repo_url":"https://github.com/ermongroup/Wifi_Activity_Recognition","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}