Journal of Medical Engineering and Technology最新文献

The determination of pressure and contact area distributions in the coxofemoral joint during activities of daily living is essential to predict joint degeneration and prosthesis wear. This can also provide biomechanical justifications for preoperative planning and postoperative rehabilitation. To study the temporal evolution of pressure fields and contact areas in a person's coxofemoral joint during different activities, a parametric finite element model of the joint is developed. Eight activities of daily living are studied. Two different laws of cartilage behaviour are used: elastic and hyperelastic. The results obtained focused on a single subject are compared with those of other studies using classical hypotheses: no labrum, synovial fluid and bone deformability neglected, ideal spherical geometry of the articular surfaces and frictionless contact. The results show that activities related to sitting in and getting up from a chair are the least burdensome activities for the hip joint. Alternation between the bipodal station and the monopodal station is the most restrictive activity. For most activities, the highest pressures are in the anterolateral upper region of the femoral head and in the antero-superior region of the cotyloid. For the activities studied, considering the hyperelasticity of cartilage does not generate a significant difference compared to a simple elastic behaviour. The results are globally in agreement with numerical and analytical models using a spherical model of the joint and quantitatively enrich the knowledge of this field.

An electroencephalogram (EEG) is a record of signals that represent surface potentials varying whenever the brain performs any task and can be recorded by placing an arrangement of electrodes at the scalp of the brain. These recordings are often contaminated by unwanted movement near these electrodes, resulting in non-cerebral signals called artefacts. The presence of artefacts makes the study of EEG signals difficult. This work focuses on a comparative analysis of classification of ocular artefacts from EEG signal that mainly comprise of eye blinks. Various feature extraction, feature selection and classification techniques are used to compare the prediction performance of the system. Three different methods were used to extract features from the EEG recording done on eight subjects, performing two different tasks. Then the diagnostic performance of three feature selection and 30 classification methods were evaluated using 5-fold cross-validation. Performance of the system on various combinations has been calculated in terms of accuracy and results have been discussed. The maximum accuracy of 93.8% was yielded by classifiers: Kernel Naïve Bayes, Linear Support Vector Machine (SVM) and Ensemble Bagged Trees using wavelet-based features, principal component analysis as feature selection algorithm. By methodically assessing 360 feature-classifier combinations, this study is innovative and provides one of the most thorough benchmarks for ocular artefact identification with exceptional accuracy. It also has great potential for real-time EEG preprocessing in clinical and BCI applications.

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.

Modern dance places relatively high requirements on dancers' balance ability, which can be enhanced through certain training. This paper mainly investigated the effects of resistance training on the balance and technical performance of female modern dancers. Forty female modern dancers from the Dance College of Northwest Normal University were randomly assigned to the instability resistance training (IRT) group or the resistance training (RT) group to undergo a 12-week training program. Balance ability and technical performance were assessed before and after the training. After the training, the balance ability and technical performance of both the IRT group and the RT group were affected to a certain extent. Specifically, the closed-eye one-legged standing time for the left and right legs in the IRT group was 37.74 ± 20.16 s and 42.36 ± 16.87 s, respectively (p < 0.05 compared to pre-experiment and the RT group). Moreover, all indices of dynamic standing stability in the IRT group showed improvement (p < 0.05 compared to pre-experiment and the RT group), and the balance move scores for the IRT group also improved significantly, with the seated low-space near-ground rotation score reaching 8.37 ± 0.56 points (p < 0.05 compared to pre-experiment and the RT group). The results demonstrate that IRT has an advantage in improving the balance ability and technical performance of female modern dancers. This method can be effectively applied in modern dance training programs. Keywords: resistance training, modern dance, technical performance, balance ability.

This scoping review mapped the literature on alternative techniques for removing fractured screws from dental implants. Following the five-step methodological framework by Arksey and O'Malley and the Joanna Briggs Institute Manual for Evidence Synthesis, the study adhered to the PRISMA-ScR checklist. The protocol was registered in the Open Science Framework (). Two independent reviewers searched MEDLINE (PubMed), Web of Science, Embase, and ClinicalTrials.gov in December 2024 using the terms "dental implants" AND ("screw retrieval" OR "fractured screw" OR "screw removal" OR "screw fragment"), including gray literature and reference lists. Among the 47 included studies, six were in vitro, one in silico, twenty-six clinical case reports, and fourteen technical descriptions. The main removal approaches identified were: (1) manual instruments; (2) ultrasonic devices; (3) mechanical or rescue kits; (4) rotary or drilling methods; and (5) customized alternatives such as laser welding, hollow screw modification, and guided drilling. No single method proved superior. The choice of technique depends on clinical conditions, fracture type, and implant preservation. Conservative, low-risk approaches should be attempted before invasive methods. Overall, prevention, torque control, and periodic maintenance remain the most effective strategies to avoid screw fractures. .

Timely recognition of dermatological manifestations caused by toxic environmental exposure is vital for effective healthcare management. Arsenic, a widespread contaminant in groundwater, has severe dermatological effects, leading to chronic disorders that often remain undiagnosed in their early stages. This study presents an advanced deep learning framework designed to support the early diagnosis of arsenic-induced skin conditions through dermoscopic image analysis. The research utilised a comprehensive dataset of 8892 dermoscopic images collected from four field sites in Bangladesh, encompassing both arsenic-exposed and unaffected individuals. Discriminative image features were extracted using a synergistic ResNet-DenseNet architecture, which captures both local textural and global contextual representations. The extracted features were subsequently classified using the k-Nearest Neighbour algorithm to distinguish arsenic-affected from healthy skin images. The proposed method achieved 99.37% classification accuracy, a 99.36% F1-score, 99.14% sensitivity and 99.59% recall, reflecting its strong diagnostic reliability. These outstanding results suggest that the framework can efficiently assist dermatologists by providing automated, consistent and objective evaluation of arsenic-related lesions. It also provides a data-driven method for monitoring public health in areas where arsenic contamination is a long-term problem. Overall, the study demonstrates the clinical potential of deep learning-based dermoscopic analysis for improving the early detection and management of arsenic-related dermatological disorders.

Purpose: Analysis of the fit of off-the-shelf knee endoprostheses in three-dimensional planes, with possible impact on the implantation results.

Methods: The implantation of three different off-the-shelf knee endoprostheses is simulated in 92 patients who were treated with custom-made knee endoprostheses in Agaplesion Ev. Klinikum Schaumburg joint centre Fit was determined in different planes using newly defined measurement variables.

Results: Significant deviation of fit in different measurement categories depending on prothesis model and patient characteristics.

Conclusions: The results of this study encourage to do preoperative analysis of patients anatomical knee shape and to perform preoperative fit simulations in defined measurement categories for different knee endoprotheses before implantation to reach optimal results.

Clinical relevance: Such algorithms may significantly improve the early postoperative results in terms of range of motion and long-term revision rates, with an impact on patient satisfaction and overall treatment costs for knee arthritis.

Wearable devices are used increasingly within the medical world, ranging from monitoring for head trauma to screening for heat injuries. Understanding what makes these devices tolerable to the end user in remote hostile environments is crucial for research, military, and humanitarian medicine, with broader translational implications. This opportunistic qualitative study trialled five different forms of wearable devices on the Interdisciplinary South Pole Innovation and Research Expedition 2022 (INSPIRE 22), an expedition which skied from the edge of the Antarctic land mass to the South Pole. It also examined the feasibility of near-real time analysis of wearable data from a hostile environment remotely from the UK. Key findings highlighted that usability of wearable devices was impacted by human-device interface factors (comfort, user buy in, and charging) and device resource requirements (power, data, and storage space on a personal mobile phone). Users required a more positive than negative aspects to maintain device interaction. Near-real-time data analysis of wearable technology from extreme environments is feasible but only on a small inconsistent scale due to limited connectivity. Reliable internet access, broader bandwidth, and better user access to data are essential to achieve meaningful health and performance insights for the individual and wider organisations.

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.