Open Access

ARTICLE

A Hybrid Deep Learning Approach for IoT-Enabled Human Activity Recognition and Advanced Analytics

Shtwai Alsubai¹, Abdullah Al Hejaili², Najib Ben Aoun^3,4,*, Amina Salhi⁵, Vincent Karovič^6,*

1 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, Tabuk, Saudi Arabia
3 Faculty of Computing and Information, Al-Baha University, Alaqiq, Saudi Arabia
4 REGIM-Lab: Research Groups in Intelligent Machines, National School of Engineers of Sfax (ENIS), University of Sfax, Sfax, Tunisia
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia
6 Department of Information Management and Business Systems, Faculty of Management, Comenius University Bratislava, Odbojárov 10, Bratislava, Slovakia

* Corresponding Authors: Najib Ben Aoun. Email: ; Vincent Karovič. Email:

(This article belongs to the Special Issue: Advances in Action Recognition: Algorithms, Applications, and Emerging Trends)

Computers, Materials & Continua 2026, 87(2), 66 https://doi.org/10.32604/cmc.2026.074057

Received 30 September 2025; Accepted 16 December 2025; Issue published 12 March 2026

Abstract

The concept of Human Activity Recognition (HAR) is integral to applications based on Internet of Things (IoT)-enabled devices, particularly in healthcare, fitness tracking, and smart environments. The streams of data from wearable sensors are rich in information, yet their high dimensionality and variability pose a significant challenge to proper classification. To address this problem, this paper proposes hybrid architectures that integrate traditional machine learning models with a deep neural network (DNN) to deliver improved performance and enhanced capabilities for HAR tasks. Multi-sensor HAR data were used to systematically test several hybrid models, including: RF + DNN (Random Forest + Deep Neural Network), XGB + DNN (XGBoost + DNN), GB + DNN (Gradient Boosting + DNN), KNN + DNN (K-Nearest Neighbors + DNN), and DT + DNN (Decision Tree + DNN). The RF + DNN model was the most accurate, achieving a 97.03% score with excellent precision, recall, and F1-score. These findings demonstrate that hybrid machine learning and deep learning systems have a promising future in IoT-based HAR applications. The model provides a novel solution for developing smart and trustworthy monitoring systems that support real-time analytics, patient surveillance, and other IoT applications.

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Keywords

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