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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

Mohamed Esmail Karar1,2,*, Omar Reyad1,3, Hazem Ibrahim Shehata1,4

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

* Corresponding Author: Mohamed Esmail Karar. Email: email

Computer Systems Science and Engineering 2023, 45(3), 2377-2389.


This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall. Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks. By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.


Cite This Article

APA Style
Karar, M.E., Reyad, O., Shehata, H.I. (2023). Deep forest-based fall detection in internet of medical things environment. Computer Systems Science and Engineering, 45(3), 2377-2389.
Vancouver Style
Karar ME, Reyad O, Shehata HI. Deep forest-based fall detection in internet of medical things environment. Comput Syst Sci Eng. 2023;45(3):2377-2389
IEEE Style
M.E. Karar, O. Reyad, and H.I. Shehata "Deep Forest-Based Fall Detection in Internet of Medical Things Environment," Comput. Syst. Sci. Eng., vol. 45, no. 3, pp. 2377-2389. 2023.

cc 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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