{"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/detecting-irregular-patterns-in-iot-streaming","title":"Detecting Irregular Patterns in IoT Streaming Data for Fall Detection","arxiv_id":"1811.06672","date":"2018-11-16","proceeding":null,"authors":["Sazia Mahfuz","Haruna Isah","Farhana Zulkernine","Peter Nicholls"],"abstract":"Detecting patterns in real time streaming data has been an interesting and\nchallenging data analytics problem. With the proliferation of a variety of\nsensor devices, real-time analytics of data from the Internet of Things (IoT)\nto learn regular and irregular patterns has become an important machine\nlearning problem to enable predictive analytics for automated notification and\ndecision support. In this work, we address the problem of learning an irregular\nhuman activity pattern, fall, from streaming IoT data from wearable sensors. We\npresent a deep neural network model for detecting fall based on accelerometer\ndata giving 98.75 percent accuracy using an online physical activity monitoring\ndataset called \"MobiAct\", which was published by Vavoulas et al. The initial\nmodel was developed using IBM Watson studio and then later transferred and\ndeployed on IBM Cloud with the streaming analytics service supported by IBM\nStreams for monitoring real-time IoT data. We also present the systems\narchitecture of the real-time fall detection framework that we intend to use\nwith mbientlabs wearable health monitoring sensors for real time patient\nmonitoring at retirement homes or rehabilitation clinics.","url_abs":"http://arxiv.org/abs/1811.06672v1","url_pdf":"http://arxiv.org/pdf/1811.06672v1.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":"detecting-irregular-patterns-in-iot-streaming","repo_url":"https://github.com/SaziaM/IEMCON2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.06672","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}