Binglei Yue, Aili Jiang, Chun Yang, Junwei Lei, Heng Liu, Yin Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.071047 - 10 November 2025

Abstract With the growing advancement of wireless communication technologies, WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution. Among the available signal types, Channel State Information (CSI) offers fine-grained temporal, frequency, and spatial insights into multipath propagation, making it a crucial data source for human-centric sensing. Recently, the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments. This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI. We first outline mainstream CSI acquisition tools and their hardware specifications, More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1389-1485, 2025, DOI:10.32604/cmes.2025.071858 - 26 November 2025

Abstract Human Activity Recognition (HAR) represents a rapidly advancing research domain, propelled by continuous developments in sensor technologies and the Internet of Things (IoT). Deep learning has become the dominant paradigm in sensor-based HAR systems, offering significant advantages over traditional machine learning methods by eliminating manual feature extraction, enhancing recognition accuracy for complex activities, and enabling the exploitation of unlabeled data through generative models. This paper provides a comprehensive review of recent advancements and emerging trends in deep learning models developed for sensor-based human activity recognition (HAR) systems. We begin with an overview of fundamental HAR… More > Graphic Abstract

Moshiur Rahman Tonmoy¹, Md. Mithun Hossain¹, Mejdl Safran^2,*, Sultan Alfarhood², Dunren Che³, M. F. Mridha⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5747-5763, 2025, DOI:10.32604/cmc.2025.067589 - 23 October 2025

Abstract Automated recognition of violent activities from videos is vital for public safety, but often raises significant privacy concerns due to the sensitive nature of the footage. Moreover, resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices. With this motivation, this study aims to investigate an effective violent activity classifier while minimizing computational complexity, attaining competitive performance, and mitigating user data privacy concerns. We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition. We utilize a two-stream formation of 3D depthwise separable convolution coupled More >

Lakshmi Alekhya Jandhyam^1,*, Ragupathy Rengaswamy¹, Narayana Satyala²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3679-3714, 2025, DOI:10.32604/cmes.2025.068941 - 30 September 2025

Abstract Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble More >

Hoang-Dieu Vu^1,2, Duc-Nghia Tran³, Quang-Tu Pham¹, Ngoc-Linh Nguyen^4,*, Duc-Tan Tran^1,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2819-2835, 2025, DOI:10.32604/cmc.2025.068355 - 23 September 2025

Abstract This study presents CGB-Net, a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer, with direct applicability to gastroesophageal reflux disease (GERD) monitoring. Unlike conventional approaches limited to four basic postures, CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions, providing enhanced resolution for personalized health assessment. The architecture introduces a unique integration of three complementary components: 1D Convolutional Neural Networks (1D-CNN) for efficient local spatial feature extraction, Gated Recurrent Units (GRU) to capture short-term temporal dependencies with reduced computational complexity, and Bidirectional Long Short-Term Memory… More >

Nayeemul Islam Nayeem¹, Shirin Mahbuba¹, Sanjida Islam Disha¹, Md Rifat Hossain Buiyan¹, Shakila Rahman^1,*, M. Abdullah-Al-Wadud², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025

Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >

Asaad Algarni¹, Iqra Aijaz Abro², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5879-5896, 2025, DOI:10.32604/cmc.2025.064601 - 30 July 2025

Abstract Inertial Sensor-based Daily Activity Recognition (IS-DAR) requires adaptable, data-efficient methods for effective multi-sensor use. This study presents an advanced detection system using body-worn sensors to accurately recognize activities. A structured pipeline enhances IS-DAR by applying signal preprocessing, feature extraction and optimization, followed by classification. Before segmentation, a Chebyshev filter removes noise, and Blackman windowing improves signal representation. Discriminative features—Gaussian Mixture Model (GMM) with Mel-Frequency Cepstral Coefficients (MFCC), spectral entropy, quaternion-based features, and Gammatone Cepstral Coefficients (GCC)—are fused to expand the feature space. Unlike existing approaches, the proposed IS-DAR system uniquely integrates diverse handcrafted features using… More >

Tatsuhito Hasegawa^#,*, Kyosuke Fujino^#

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2429-2454, 2025, DOI:10.32604/cmc.2025.066270 - 03 July 2025

Abstract Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare, sports, and other domains but this has also raised critical privacy concerns, especially under tightening regulations such as the General Data Protection Regulation (GDPR), which explicitly restrict the processing of data that can re-identify individuals. Although existing anonymization approaches such as the Anonymizing AutoEncoder (AAE) can reduce the risk of re-identification, they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task (e.g., human activity recognition). This study proposes a novel sensor data anonymization method based… More >

Subrata Kumer Paul^1,2, Abu Saleh Musa Miah^3,4, Rakhi Rani Paul^1,2, Md. Ekramul Hamid², Jungpil Shin^4,*, Md Abdur Rahim⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2513-2530, 2025, DOI:10.32604/cmc.2025.063563 - 03 July 2025

Abstract The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Md. Maniruzzaman³, Satoshi Nishimura¹, Sultan Alfarhood⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3615-3638, 2025, DOI:10.32604/cmes.2025.064588 - 30 June 2025

Abstract Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set… More > Graphic Abstract