{"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/rf-based-fall-monitoring-using-convolutional","title":"RF-Based Fall Monitoring Using Convolutional Neural Networks","arxiv_id":null,"date":"2018-09-01","proceeding":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018 9","authors":["Yonglong Tian","Guang-He Lee","Hao He","Chen-Yu Hsu","Dina Katabi"],"abstract":"Falls are the top reason for fatal and non-fatal injuries among seniors. Existing solutions are based on wearable fall-alert sensors, but medical research has shown that they are ineffective, mostly because seniors do not wear them. These revelations have led to new passive sensors that infer falls by analyzing Radio Frequency (RF) signals in homes. Seniors can go about their lives as usual without the need to wear any device. While passive monitoring has made major advances, current approaches still cannot deal with the complexities of real-world scenarios. They typically train and test their classifiers on the same people in the same environments, and cannot generalize to new people or new environments. Further, they cannot separate motions from different people and can easily miss a fall in the presence of other motions.\r\n\r\nTo overcome these limitations, we introduce Aryokee, an RF-based fall detection system that uses convolutional neural networks governed by a state machine. Aryokee works with new people and environments unseen in the training set. It also separates different sources of motion to increase robustness. Results from testing Aryokee with over 140 people performing 40 types of activities in 57 different environments show a recall of 94% and a precision of 92% in detecting falls.","url_abs":"https://doi.org/10.1145/3264947","url_pdf":"http://people.csail.mit.edu/yonglong/yonglong/rffall.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":[],"tasks":[{"task_slug":"rf-based-pose-estimation","task_name":"RF-based Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/rf-based-pose-estimation-on-rf-mmd","task":"RF-based Pose Estimation","dataset":"RF-MMD","model":"Aryokee","rank_in_archive_order":2,"of":2,"metrics":{"mAP (@0.1, Through-wall)":"72.9","mAP (@0.1, Visible)":"78.3"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}