Fall Detection System using XGBoost and IoT

Authors

  • D.K. Cahoolessur University of Mauritius Author
  • B. Rajkumarsingh University of Mauritius Author

DOI:

https://doi.org/10.17159/2309-8988/2020/v36a2

Keywords:

fall detection, machine learning, XGBoost, IoT

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 Biographies

  • D.K. Cahoolessur, University of Mauritius

    Faculty of Engineering, University of Mauritius

  • B. Rajkumarsingh, University of Mauritius

    Department of Electrical and Electronic Engineering, University of Mauritius

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Published

20-08-2020

Issue

Section

Articles

How to Cite

“Fall Detection System using XGBoost and IoT” (2020) R&D Journal, 36, pp. 8–18. doi:10.17159/2309-8988/2020/v36a2.