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

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1087-1116, 2025, DOI:10.32604/cmes.2025.068779 - 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 More > Graphic Abstract

Nadhmi A. Gazem¹, Sultan Noman Qasem^2,3, Umair Naeem⁴, Shahid Latif⁵, Ibtehal Nafea⁶, Faisal Saeed⁷, Mujeeb Ur Rehman^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 407-427, 2025, DOI:10.32604/cmes.2025.066517 - 31 July 2025

Abstract Falls are a leading cause of injury and morbidity among older adults, especially those with Alzheimer’s disease (AD), who face increased risks due to cognitive decline, gait instability, and impaired spatial awareness. While wearable sensor-based fall detection systems offer promising solutions, their effectiveness is often hindered by domain shifts resulting from variations in sensor placement, sampling frequencies, and discrepancies in dataset distributions. To address these challenges, this paper proposes a novel unsupervised domain adaptation (UDA) framework specifically designed for cross-dataset fall detection in Alzheimer’s disease (AD) patients, utilizing advanced transfer learning to enhance generalizability. The… More >

Nizar Zaghden¹, Emad Ibrahim², Mukaram Safaldin^2,*, Mahmoud Mejdoub³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1117-1147, 2025, DOI:10.32604/cmc.2025.061948 - 26 March 2025

Abstract The increasing elderly population has heightened the need for accurate and reliable fall detection systems, as falls can lead to severe health complications. Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques. This study introduces an advanced fall detection model integrating YOLOv8, Faster R-CNN, and Generative Adversarial Networks (GANs) to enhance accuracy and robustness. A modified YOLOv8 architecture serves as the core, utilizing spatial attention mechanisms to improve critical image regions’ detection. Faster R-CNN is employed for fine-grained human posture analysis, while GANs… More >

Jyun-Guo Wang^*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1149-1170, 2024, DOI:10.32604/csse.2024.052931 - 13 September 2024

Abstract In many Eastern and Western countries, falling birth rates have led to the gradual aging of society. Older adults are often left alone at home or live in a long-term care center, which results in them being susceptible to unsafe events (such as falls) that can have disastrous consequences. However, automatically detecting falls from video data is challenging, and automatic fall detection methods usually require large volumes of training data, which can be difficult to acquire. To address this problem, video kinematic data can be used as training data, thereby avoiding the requirement of creating… More >

Kun Fang, Julong Pan^*, Lingyi Li, Ruihan Xiang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008 - 30 January 2024

Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance… More >

Sangeeta Yadav¹, Preeti Gulia^1,*, Nasib Singh Gill^1,*, Piyush Kumar Shukla², Arfat Ahmad Khan³, Sultan Alharby⁴, Ahmed Alhussen⁴, Mohd Anul Haq⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869 - 30 January 2024

Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the… More >

Xiaorui Zhang^1,2,3,*, Qijian Xie¹, Wei Sun^3,4, Yongjun Ren^1,2,3, Mithun Mukherjee⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 47-61, 2023, DOI:10.32604/cmc.2023.042561 - 31 October 2023

Abstract Fall behavior is closely related to high mortality in the elderly, so fall detection becomes an important and urgent research area. However, the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy. To solve the above problems, this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose. Lightweight OpenPose uses MobileNet as a feature extraction network, and the prediction layer uses bottleneck-asymmetric structure, thus reducing the amount of the network. The bottleneck-asymmetrical structure compresses the number of input… More >

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

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084 - 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… More >

Majdy M. Eltahir¹, Adil Yousif², Fadwa Alrowais³, Mohamed K. Nour⁴, Radwa Marzouk⁵, Hatim Dafaalla⁶, Asma Abbas Hassan Elnour⁶, Amira Sayed A. Aziz⁷, Manar Ahmed Hamza^8,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.034037 - 31 March 2023

Abstract The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3371-3385, 2023, DOI:10.32604/iasc.2023.036551 - 15 March 2023

Abstract Falls are the contributing factor to both fatal and nonfatal injuries in the elderly. Therefore, pre-impact fall detection, which identifies a fall before the body collides with the floor, would be essential. Recently, researchers have turned their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection solutions typically use either a threshold-based or machine learning-based approach, although the threshold value would be difficult to accurately determine in threshold-based methods. Moreover, while additional features could sometimes assist in categorizing falls and non-falls more precisely, the estimated determination of the significant features would be… More >