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Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm

Ming-Chih Chen, Yin-Ting Cheng*, Ru-Wei Chen

Department of Electronic Engineering, National Kaohsiung University of Science and Technology (First Campus), Kaohsiung City, 82445, Taiwan

* Corresponding Author: Yin-Ting Cheng. Email:

(This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)

Computer Modeling in Engineering & Sciences 2021, 128(3), 1103-1119.


This work presents a fall detection system based on artificial intelligence. The system incorporates miniature wearable devices for fall detection. Fall detection is achieved by integrating a three-axis gyroscope and a three-axis accelerometer. The system gathers the differential data collected by the gyroscope and accelerometer, applies artificial intelligence algorithms for model training and constructs an effective model for fall detection. To provide easy wearing and effective position detection, it is designed as a small device attached to the user’s waist. Experiment results have shown that the accuracy of the proposed fall detection model is up to 98%, demonstrating the effectiveness of the model in real-life fall detection.


Cite This Article

Chen, M., Cheng, Y., Chen, R. (2021). Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm. CMES-Computer Modeling in Engineering & Sciences, 128(3), 1103–1119.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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