Journal of Medical Engineering and Technology最新文献

Hand tremors are among the most prevalent neurodegenerative movement disorders, causing involuntary upper-limb oscillations that significantly impair patients' quality of life. While medications and therapy provide limited relief, wearable tremor suppression devices offer a promising non-invasive alternative. A hand tremor absorber, typically passive or active, is designed to counteract involuntary shaking through mechanical or electronic means. The importance of the proposed design lies in its ability to deliver high-performance, multi-axial tremor suppression without motors, power sources, or restrictive bracing, addressing critical gaps in comfort, wearability, and real-world usability that limit existing solutions. This paper presents the analysis and optimisation of a novel passive, omnidirectional hand tremor absorber that achieves substantial amplitude reduction while preserving natural hand motion. Using a full-scale mannequin arm tremor simulator and MATLAB-based parametric modelling (MathWorks Inc., Natick, MA), key design parameters were optimised across the clinically relevant 3-7 Hz frequency range. Results demonstrate up to 79% unidirectional and 73% omnidirectional tremor suppression. A compact, donut-shaped orthosis integrating dual perpendicular absorbers was developed to effectively dampen complex, multi-directional tremors, achieving ∼75% reduction in severe cases with a total device weight of only 330 g. By combining passive operation, lightweight ergonomics, and multi-axis efficacy, this design offers a practical, patient-centered solution that overcomes the bulk, cost, and invasiveness of current alternatives. Future work will validate these results in human trials to assess real-world impact on functional independence and quality of life.

Atherosclerosis poses a significant health burden globally, contributing to a major proportion of all deaths in westernised societies. Atherosclerosis involves deposition of fat, cholesterol, calcium, and other substances on the inner walls of the artery, collectively called plaques, which finally manifests in various clinical forms, such as ischaemic heart disease and stroke. There have been consistent efforts to characterise and analyse the severity of plaques to devise comprehensive strategies targeting risk factors, early detection, and effective management. This article presents a broad overview of the mechanical characterisation of human atherosclerotic plaque, drawing from a diverse array of technical literature. The studies emphasise the importance of accurately assessing the mechanical behaviour of these plaques to better understand their pathophysiology and clinical implications. Advanced techniques, including experimental and computational hybrid approaches, provide insights into the complex mechanical properties of atherosclerotic plaques. In-silico analysis is found to be a valuable tool for investigating the mechanical behaviour of atherosclerotic tissues, particularly in plaques with softer fibrotic tissues. Overall, this review underscores the importance of advancing our understanding of the mechanical properties of human atherosclerotic plaque for improved risk stratification, patient management, and the development of targeted interventions to mitigate the burden of cardiovascular diseases.

The impacts of cognitive tasks on the brain through Electroencephalogram (EEG) signal analysis have commonly employed machine learning models like Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), random forests etc. However, these traditional models may encounter limitations in effectively addressing the unique challenges inherent in EEG signal analysis, including high dimensionality and the potential presence of noise and artefacts. This critique underscores the need for advanced methodologies, capable of navigating these challenges to enhance the accuracy and reliability of cognitive task-related EEG studies. This study addresses the challenge of accurately detecting cognitive states using EEG signals. This helps in overcoming the challenges by high dimensionality, non-stationarity, and noise in raw EEG data. Traditional classifiers such as SVMs and ANNs often fail to fully exploit the temporal and frequency features present in EEG. In order to overcome these limitations, this work introduces a novel Deep Forest-based classification model. This model is optimised through XGBoost for feature selection. A self-acquired dataset using the g.Nautilus EEG device from 12 student volunteers performing cognitive tasks forms the basis of the evaluation. A total of 48 features, spanning time, frequency, entropy, and autoregressive domains, are extracted. The proposed model achieves high classification accuracy (99.692%) while reducing computational time. This method demonstrates strong potential for real-time cognitive monitoring in neuroscience and human-computer interaction contexts.

The COVID-19 pandemic emphasised necessity for prompt, precise diagnostics, secure data storage, and robust privacy protection in healthcare. Existing diagnostic systems often suffer from limited transparency, inadequate performance, and challenges in ensuring data security and privacy. The research proposes a novel privacy-preserving diagnostic framework, Heterogeneous Convolutional-recurrent attention Transfer learning based ResNeXt with Modified Greater Cane Rat optimisation (HCTR-MGR), that integrates deep learning, Explainable Artificial Intelligence (XAI), and blockchain technology. The HCTR model combines convolutional layers for spatial feature extraction, recurrent layers for capturing spatial dependencies, and attention mechanisms to highlight diagnostically significant regions. A ResNeXt-based transfer learning backbone enhances performance, while the MGR algorithm improves robustness and convergence. A trust-based permissioned blockchain stores encrypted patient metadata to ensure data security and integrity and eliminates centralised vulnerabilities. The framework also incorporates SHAP and LIME for interpretable predictions. Experimental evaluation on two benchmark chest X-ray datasets demonstrates superior diagnostic performance, achieving 98-99% accuracy, 97-98% precision, 95-97% recall, 99% specificity, and 95-98% F1-score, offering a 2-6% improvement over conventional models such as ResNet, SARS-Net, and PneuNet. These results underscore the framework's potential for scalable, secure, and clinically trustworthy deployment in real-world healthcare systems.

The study aims to provide structural insights into stress distribution patterns, structural integrity, and the efficacy of intervention techniques, offering implications for orthopaedic practices. Under a simulated body weight of 750 N, the intact femur exhibits optimal structural integrity with uniform stress distribution (27.96 MPa), highlighting inherent strength and stability. In contrast, the presence of a 2 mm crack significantly alters stress distribution, creating localised areas of elevated stress (146.3 MPa). The crack-fixed femur analysis demonstrates successful stress reduction around the crack site. Through optimisation of scaffold morphologies, a scaffold with strut diameter of 500 μm and pore size of 450 μm was selected for insertion into the bone cavity along with a fixation structure. The maximum stress concentrations at bone are consistently below 80 MPa. This design ensures the effective distribution of physiological forces on the bone. Comparatively, the healthy femur serves as a baseline for optimal stress distribution, while the cracked femur underscores the adverse impact of fractures, necessitating early detection and interventions. The findings contribute to the ongoing development of orthopaedic practices, emphasising stability, healing, and improved patient outcomes.

Accurate simulation of respiratory dynamics is essential for advancing the diagnosis and treatment of pulmonary diseases. This review analyzes current methodologies for modelling lung mechanics during insufflation and exsufflation, focusing on airflow simulations in the tracheobronchial tree. 45 studies were selected through a structured screening process and evaluated based on modelling approaches, simulation techniques, boundary conditions, and clinical applicability. The review identifies three main strategies for 3D TB model generation: segmentation of DICOM images, CAD-based geometries, and hybrid methods. While DICOM segmentation ensures anatomical realism, it is limited in generational depth. Conversely, CAD and hybrid approaches extend model coverage but may compromise subject specificity. Simulation methods include Computational Fluid Dynamics, Fluid-Structure Interaction, biomechanical, structural and statistical models, MR-Linac workflows, and neural networks. Among these, CFD remains the most widely adopted due to its accessibility and maturity, whereas FSI and hybrid CFD-FSI models offer superior physiological fidelity. The review wants to highlight the importance of combining detailed anatomical modelling with dynamic simulation frameworks to improve clinical interventions, particularly in lung surgery. Future work should focus on integrating patient-specific imaging, advanced boundary conditions, and multiscale modelling to enable more precise and scalable respiratory simulations.

The growing need for efficient patient lifting and transfer solutions highlights a significant gap in current healthcare systems, particularly in affordable, accessible options for home use. While most research has focused on automated or motorised systems, this study introduces a novel manual patient lifting device based on a worm gear mechanism, which, despite its proven industrial benefits, remains underexplored in healthcare. Using a case study of a 50-year-old, 72 kg individual, we developed a cost-effective, manually operated lifting system aimed at reducing caregiver workload and improving patient mobility. The design was modelled using SolidWorks and subjected to comprehensive static and dynamic structural analysis under loads of 800 N, 1000 N and 1200 N. Results show that the worm gear mechanism reduces required torque by up to 66% and applied force by 15% compared to traditional lead screw systems, significantly enhancing ergonomic efficiency. Additionally, lifting speed improves by approximately 10 mm/s, and the device achieves a safety factor of 2.9 under maximum load, ensuring structural reliability. Importantly, the non-back driveable feature of the worm gear prevents unintended descent, addressing a key safety concern in manual lifting devices. This mechanically optimised and ergonomically designed solution is tailored for homecare settings, where affordability, ease of use, and portability are crucial. By applying advanced mechanical principles to a simple, reliable design, this work contributes to the development of practical assistive technologies that improve both caregiver safety and patient independence, marking a meaningful step forward in assistive healthcare technology.

The precise measurement of bite force is vital in dental diagnostics, particularly for evaluating tooth restorations, prosthetic interventions, and orthodontic treatments. This study presents the calibration and evaluation of the Flexiforce A301 sensor using optimised low-drive voltage circuits to extend its measurement range. Three circuit configurations, a voltage divider, a feedback resistor, and a feedback resistor with a capacitor were designed, simulated using LTspice, and experimentally validated. Results indicate that the configuration incorporating a feedback resistor provides superior linearity and stability, accurately measuring forces up to 100 kg, outperforming other configurations. This advancement enhances the reliability and range of bite force measurements, offering a robust foundation for high-force dental applications.