{"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/wisture-rnn-based-learning-of-wireless","title":"Wisture: RNN-based Learning of Wireless Signals for Gesture Recognition in Unmodified Smartphones","arxiv_id":"1707.08569","date":"2017-07-26","proceeding":null,"authors":["Mohamed Abudulaziz Ali Haseeb","Ramviyas Parasuraman"],"abstract":"This paper introduces Wisture, a new online machine learning solution for\nrecognizing touch-less dynamic hand gestures on a smartphone. Wisture relies on\nthe standard Wi-Fi Received Signal Strength (RSS) using a Long Short-Term\nMemory (LSTM) Recurrent Neural Network (RNN), thresholding filters and traffic\ninduction. Unlike other Wi-Fi based gesture recognition methods, the proposed\nmethod does not require a modification of the smartphone hardware or the\noperating system, and performs the gesture recognition without interfering with\nthe normal operation of other smartphone applications.\n  We discuss the characteristics of Wisture, and conduct extensive experiments\nto compare its performance against state-of-the-art machine learning solutions\nin terms of both accuracy and time efficiency. The experiments include a set of\ndifferent scenarios in terms of both spatial setup and traffic between the\nsmartphone and Wi-Fi access points (AP). The results show that Wisture achieves\nan online recognition accuracy of up to 94% (average 78%) in detecting and\nclassifying three hand gestures.","url_abs":"http://arxiv.org/abs/1707.08569v2","url_pdf":"http://arxiv.org/pdf/1707.08569v2.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":"wisture-rnn-based-learning-of-wireless","repo_url":"https://github.com/mohaseeb/wisture","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"}],"methods":[],"datasets_introduced":[{"slug":"wisture-dataset","name":"Wisture Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}