Open Access

ARTICLE

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Omar Reyad^1,2, Hazem Ibrahim Shehata^1,3, Mohamed Esmail Karar^1,4,*

1 College of Computing and Information Technology, Shaqra University, P. O. Box 33, Shaqra, 11961, Saudi Arabia
2 Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, 82524, Egypt
3 Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt
4 Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

* Corresponding Author: Mohamed Esmail Karar. Email:

(This article belongs to the Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)

Computers, Materials & Continua 2023, 76(2), 1689-1700. https://doi.org/10.32604/cmc.2023.039084

Received 10 January 2023; Accepted 26 May 2023; Issue published 30 August 2023

Abstract

Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically optimized. This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system. The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch. Each scenario in this dataset is classified and labeled as a fall or a non-fall. In comparison to the three machine learning models—K-nearest neighbors (KNN), decision tree (DT), and standard random forest (SRF), the proposed ensemble classifier outperformed the other models and achieved 98.4% accuracy. The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

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Keywords

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