A New Fracture Liaison Service Using the Mobile Application and IoT Sensor.

Clicks: 434
ID: 83196
2019
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Ranked #11 of 127 articles by views in conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference

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Abstract
While the clinical design of a Fracture Liaison Service(FLS) has been used as a localized healthcare service in a previous study, thus far there has not been an international mobile application, such as a FLS using smart phones. In addition, we developed a safety monitoring system using IoT sensor for a smart wheelchair. Our FLS is able to give patient various fall-related predictions with a safety monitoring system on this mobile application. The goal of our study is to improve the prevention of secondary fractures from our FLS application. We have developed a Fracture Liaison Service as an Android-OS application and released this service as a secondary fracture prevention program for osteoporotic fracture patients. We have released the final version of the FLS mobile application in Google's PlayStore. The new model of the FLS mobile application can be practically commercialized, and the effective second-order fracture prevention system is based on an open policy platform. We hope to contribute to the prevention and management of osteoporotic fractures and osteoporosis worldwide via this FLS mobile application. In the future, an intelligent personal FLS is definitely possible, by applying a Medical AI based on a huge DB.
Reference Key
kim2019aconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kim, Sung-Woo;Won, Young-Jun;Chae, Dong-Sik;Chang, Hyuk-Jae;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2019
DOI
10.1109/EMBC.2019.8857094
URL
Keywords
random forest iot wsn amof intrusion detection systems linear regression

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