[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125131-en":3,"doc-seo-125131-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125131,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Machine learning classifiers for fall detection leveraging LoRa communication network - Abstract","Today, health monitoring increasingly depends on technological advances. This study presents a low-power wide-area network (LPWAN) multinodal system for monitoring vital physiological data, using two nodes—an indoor master node and an outdoor sensing node—connected through LoRa long-range transceivers. The outdoor node employs an MPU6050 module and measures heart rate, oxygen pulse, temperature, and skin resistance, then sends data via Adafruit cloud. Coverage reaches 4.5 km with an optimal 4 km spacing. Multiple machine-learning classifiers are evaluated, and the decision tree achieves 0.99864 accuracy, supporting early fall detection and risk reduction for elderly users.","Machine learning classifiers for fall detection leveraging LoRa  \ncommunication network  \nI. V. Subba Reddy1, P. Lavanya2, V. Selvakumar2  \n1Department of Physics, GITAM (Deemed to be University), Hyderabad, India  \n2Bhavan's Vivekananda College of Science, Humanities and Commerce, Hyderabad, India  \n\n| Article history:\u003Cbr>Received Mar 15, 2023 Revised Aug 28, 2023 Accepted Sep 7, 2023 | Today, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>F1 score\u003Cbr>Long range Machine learning MPU6050\u003Cbr>Precision\u003Cbr>Remote health monitoring |  |\n\nCorresponding Author:  \nI. V. Subba Reddy  \nDepartment of Physics, GITAM (Deemed to be University) Rudraram, Patancheru mandal, Hyderabad-502329, Telangana, India [Email: vimmared@gitam.edu](Email: vimmared@gitam.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn today's world, technological advancements have become indispensable in the field of human health monitoring. With the advancement of data mining and artificial intelligence technologies integrated with systems monitoring human posture and other physiological parameters, it is significantly easier to determine how people's habits and activities impact their health and longevity [1]. Hospitals and doctors play an essential part in conventional health monitoring, but this approach necessitates significant time commitments for patient preparation, appointment making, patient waiting, doctor consultation and checkup.  \nDue to the difficulties of contacting professional specialists in person, adopting low-cost internet of things (IoT) technologies for patient health monitoring is essential [2] . Smart cities emphasize more innovative living and transportation and structured and intelligent health monitoring systems. On the other hand, supervising the daily activities of some particular populations, such as youngsters and the elderly, ensures that they get daily exercise. Using wearable sensors as an early diagnostic tool, noninvasively assessing vital parameters such as respiration rate, body temperature, and blood oxygen level tool, noninvasively assessing offers considerable promise [3] . Health and fitness status tracking of people has been one area in which this kind of technology has proved to be very beneficial. Miniaturized, unobtrusive, and ubiquitous gadgets are used to communicate and change their behavior according to the preferences of the user [4] . The IoT concept,  \nwhich clearly refers to devices permanently connected to communication networks, has transformed how we use different technology, allowing for a wide variety of applications that rely on elements like a population's economic condition and digital abilit","cbCaimgNxtaKrdW3","https://ap.wps.com/l/cbCaimgNxtaKrdW3","pdf",429210,1,9,"English","en",105,"# Article Info ABSTRACT\n## 1. 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