Open Access

ARTICLE

Towards Secure and Efficient Human Fall Detection: Sensor-Visual Fusion via Gramian Angular Field with Federated CNN

Md Sabir Hossain¹, Md Mahfuzur Rahman^1,2,*, Mufti Mahmud^1,3

1 Department of Information and Computer Science, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
2 Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
3 SDAIA-KFUPM Joint Research Center for AI and Interdisciplinary Research Center for Bio Systems and Machines, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

* Corresponding Author: Md Mahfuzur Rahman. Email:

(This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)

Computer Modeling in Engineering & Sciences 2025, 145(1), 1087-1116. https://doi.org/10.32604/cmes.2025.068779

Received 06 June 2025; Accepted 22 September 2025; Issue published 30 October 2025

Abstract

This article presents a human fall detection system that addresses two critical challenges: privacy preservation and detection accuracy. We propose a comprehensive framework that integrates state-of-the-art machine learning models, multimodal data fusion, federated learning (FL), and Karush-Kuhn-Tucker (KKT)-based resource optimization. The system fuses data from wearable sensors and cameras using Gramian Angular Field (GAF) encoding to capture rich spatial-temporal features. To protect sensitive data, we adopt a privacy-preserving FL setup, where model training occurs locally on client devices without transferring raw data. A custom convolutional neural network (CNN) is designed to extract robust features from the fused multimodal inputs under FL constraints. To further improve efficiency, a KKT-based optimization strategy is employed to allocate computational tasks based on device capacity. Evaluated on the UP-Fall dataset, the proposed system achieves 91% accuracy, demonstrating its effectiveness in detecting human falls while ensuring data privacy and resource efficiency. This work contributes to safer, scalable, and real-world-applicable fall detection for elderly care.

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