{"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/online-fall-detection-using-recurrent-neural","title":"Online Fall Detection using Recurrent Neural Networks","arxiv_id":"1804.04976","date":"2018-04-13","proceeding":null,"authors":["Mirto Musci","Daniele De Martini","Nicola Blago","Tullio Facchinetti","Marco Piastra"],"abstract":"Unintentional falls can cause severe injuries and even death, especially if\nno immediate assistance is given. The aim of Fall Detection Systems (FDSs) is\nto detect an occurring fall. This information can be used to trigger the\nnecessary assistance in case of injury. This can be done by using either\nambient-based sensors, e.g. cameras, or wearable devices. The aim of this work\nis to study the technical aspects of FDSs based on wearable devices and\nartificial intelligence techniques, in particular Deep Learning (DL), to\nimplement an effective algorithm for on-line fall detection. The proposed\nclassifier is based on a Recurrent Neural Network (RNN) model with underlying\nLong Short-Term Memory (LSTM) blocks. The method is tested on the publicly\navailable SisFall dataset, with extended annotation, and compared with the\nresults obtained by the SisFall authors.","url_abs":"http://arxiv.org/abs/1804.04976v1","url_pdf":"http://arxiv.org/pdf/1804.04976v1.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":"online-fall-detection-using-recurrent-neural","repo_url":"https://bitbucket.org/unipv_cvmlab/sisfalltemporallyannotated","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}