@article{Alsubai2026, 
author = {Shtwai Alsubai and Abdullah Al Hejaili and Najib Ben Aoun and Amina Salhi and Vincent Karovič},
title = {A Hybrid Deep Learning Approach for IoT-Enabled Human Activity Recognition and Advanced Analytics},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {66},
keywords = {Human activity recognition (HAR), Internet of Things (IoT), wearable sensors, hybrid models, deep neural networks (DNN)},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.074057},
doi = {10.32604/cmc.2026.074057},
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.}
}