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      Fall Detection System using XGBoost and IoT

      research-article
      ,
      R&D Journal
      South African Institution of Mechanical Engineering
      Fall detection, machine learning, XGBoost, IoT

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          Abstract

          This project aims to design and implement a fall detection system for the elders using machine learning techniques and Internet-of-Things (IoT). The main issue with fall detection systems is false alarms and hence incorporating machine learning in the fall detection algorithm can tackle this problem. Therefore, choosing the right machine learning algorithm for the given problem is essential and several factors need to be considered in making that choice. For this project, the XGBoost algorithm is used and the machine learning model is trained on the Sisfall dataset. A wearable device that is worn on the waist is designed using an accelerometer, a microcontroller, a Global Positioning System (GPS) module and a buzzer. The acceleration data obtained is converted into features and fed into the machine learning model which will then make a prediction. If a fall event has occurred, the buzzer is activated and emergency contacts of the victim are notified immediately using IoT and Global System for Mobile Communications (GSM). This allows the fall victim to be attended quickly, thus reducing the negative consequences of the fall. The details of the fall are stored on the cloud so that they can be easily accessed by healthcare professionals. Testing the system concluded that the XGBoost machine learning algorithm is well suited for this problem due to the small percentage error obtained.

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          Author and article information

          Journal
          rd
          R&D Journal
          R&D j. (Matieland, Online)
          South African Institution of Mechanical Engineering (Stellenbosch, Cape Town, Western Cape Province, South Africa )
          0257-9669
          2309-8988
          2020
          : 36
          : 8-18
          Affiliations
          [02] orgnameUniversity of Mauritius orgdiv1Department of Electrical and Electronic Engineering
          [01] orgnameUniversity of Mauritius orgdiv1Faculty of Engineering
          Article
          S2309-89882020000100003 S2309-8988(20)03600000003
          10.17159/2309-8988/2020/v36a2
          387fa9a6-d945-48ab-aa54-fc4f1621532c

          This work is licensed under a Creative Commons Attribution 4.0 International License.

          History
          : 12 June 2020
          : 30 October 2019
          : 26 November 2019
          Page count
          Figures: 0, Tables: 0, Equations: 0, References: 26, Pages: 11
          Product

          SciELO South Africa

          Categories
          Articles

          Fall detection,IoT,XGBoost,machine learning
          Fall detection, IoT, XGBoost, machine learning

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