Rashid Jahangir^1,*, Nazik Alturki², Muhammad Asif Nauman³, Faiqa Hanif¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071574 - 09 December 2025

Abstract Human activity recognition (HAR) is a method to predict human activities from sensor signals using machine learning (ML) techniques. HAR systems have several applications in various domains, including medicine, surveillance, behavioral monitoring, and posture analysis. Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately. Several research studies on HAR have utilized Mel frequency cepstral coefficients (MFCCs) because of their effectiveness in capturing the periodic pattern of sensor signals. However, existing MFCC-based approaches often fail to capture sufficient temporal variability, which limits their ability to distinguish… More >

A. X. Yang^a, L. F. Huang^b,*, Y. Y. Liu^c

Chalcogenide Letters, Vol.22, No.7, pp. 649-663, 2025, DOI:10.15251/CL.2025.227.649

Abstract An innovative flexible electronic device was developed by integrating functionalized carbon nanotubes, bismuth sulfide nanostructures, and a polyvinylidene fluoride matrix to create a highly water‐resistant strain detection platform. The fabricated film exhibited a remarkable static water contact angle of 141°, with only a 3–4° reduction after 48 hours of immersion, confirming its excellent hydrophobic performance. Mechanical testing revealed a tensile strength of 43.2 MPa and maintained over 96% of its original strength following 1000 bending cycles, thereby demonstrating outstanding durability under repetitive deformation. Electrical characterization showed an initial conductivity of 12.3 S/m and a baseline resistance near… More >

Junyu Ge¹, Yifan Wang¹, Hong Li^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-3, 2025, DOI:10.32604/icces.2025.011446

Abstract The advancement of untethered and adaptive robotic systems necessitates the development of compact, efficient, and silent pneumatic power sources [1,2]. Traditional pneumatic actuation relies on bulky compressors or external gas reservoirs, limiting their practical applications in mobile and autonomous systems [3,4]. This work presents a novel electrochemical pneumatic battery (EPB) that exploits electrochemical driven gas generation to achieve controlled and energy-efficient pneumatic actuation, offering a viable alternative to conventional air supply methods. The EPB operates through an electrochemical redox mechanism based on a zinc-oxygen battery [5–7], enabling reversible gas storage and controlled pressure modulation. This… More >

Shifa Fan^1,*, Yuanwen Gao^2,3, Zhiqiang Li¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.010668

Abstract In recent years, wearable technology has burst onto the scene as a game-changer, completely transforming multiple facets of our daily lives—from keeping tabs on our health to facilitating communication for staying connected. It has found its way into diverse fields such as healthcare, education, the military, engineering, and sports. However, a major challenge hindering the popularization of wearable devices is the need for a reliable power source. Conventional batteries, though widely used, have limitations, including the need for frequent recharging or replacement, which hinder the seamless integration of wearable technology into everyday life [1]. To… More >

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Mohammad Soleimani Amiri¹, Sahbi Boubaker^2,3,*, Rizauddin Ramli^4,*, Souad Kamel²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3113-3133, 2025, DOI:10.32604/cmes.2025.069167 - 30 September 2025

Abstract Disability is defined as a condition that makes it difficult for a person to perform certain vital activities. In recent years, the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent. However, controlling an exoskeleton for rehabilitation presents challenges due to their non-linear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton. To remedy these problems, this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons, addressing the challenges of nonlinear dynamics and external disturbances. The proposed controller integrated… More >

Florentin Smarandache¹, Saleh I. Alzahrani², Sulaiman Al Amro³, Ijaz Ahmad⁴, Mubashir Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3715-3735, 2025, DOI:10.32604/cmes.2025.068736 - 30 September 2025

Abstract Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body. Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes, where abnormal hemoglobin levels can indicate significant health issues. Traditional methods for hemoglobin measurement are invasive, causing pain, risk of infection, and are less convenient for frequent monitoring. PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure, sleep, blood glucose, and stress analysis. In this work, we propose a hemoglobin estimation method using an adaptive lightweight… More >

Kaiwei Tang^1,2,*, Xiufeng Wang^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010499

Abstract The prolonged health monitoring using wearable technology faces challenges stemming from perspiration, including bacterial proliferation, compromised adhesion, signal quality deterioration, and user discomfort. Notably, excessive sweat fosters bacterial colonization, escalating infection risks, and compromising biomarker analysis. Existing antibacterial approaches, unfortunately, risk disrupting the delicate balance of skin microbiota. To address this, we’ve developed a Janus patch featuring Zn-Al layered double hydroxide (LDH) modification, which boasts sustained antibacterial properties while preserving the epidermal microecology. It integrates a hydrophobic LDH fabric that mechanically eradicate bacteria via a nanoknife effect, and a laser-engraved medical adhesive with microholes for More >

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 >

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 >