Tremors are defined as low to medium-frequency oscillations of the human limbs. These tremors are typically a result of chemical imbalances in the brain that lead to involuntary or uncontrolled voluntary movements of the human arm. Numerous medical treatments have been devised to control tremors, but they can be unsuccessful and expensive, with some undesirable side effects in the long run. This paper introduces a passive actuator capable of attenuating tremors over a wide range of frequencies while being lightweight and small in size. The tremors are modelled as harmonic vibrations, and the arm is modelled as a lumped mass for the shoulder flexion-extension degree of freedom. The device produces 99.34 % tremor reduction at resonance and an average tremor reduction of 55 % between 0.8 and 8Hz.

Wireless Body Area Networks (WBANs) are vital for real-time health monitoring in eHealth systems. This article presents a comprehensive comparative analysis of MAC protocols based on IEEE 802.15.6 and IEEE 802.15.4 standards, with a focus on energy efficiency, latency, reliability, and emergency data handling. We critically examine superframe structures, access mechanisms, and adaptive MAC designs, and introduce a five-dimensional framework for protocol evaluation. Our study identifies key limitations in existing solutions—such as lack of support for emergency traffic and mobility adaptation—and outlines future research directions for developing intelligent, QoS-aware, and energy-efficient MAC protocols tailored to heterogeneous WBAN environments.

Prostate cancer is among the most diagnosed malignancies in men worldwide and a leading cause of cancer-related mortality. Early and accurate diagnosis is critical to improve patient outcomes and reduce the risks of overtreatment or missed detection. Conventional diagnostic approaches, including prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological analysis, often suffer from limited sensitivity and specificity, leading to false positive or delayed diagnosis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has recently emerged as an effective modality for prostate cancer detection, providing complementary anatomical and functional information. This study proposes a novel hybrid diagnostic framework that integrates Generalized Quantum Gamma Polynomial (GQGP) features, kinetic signal intensity features, and deep learning-based representations. GQGP features capture subtle intensity variations and quantum-inspired statistical characteristics, while kinetic features quantify contrast-enhancement dynamics to discriminate malignant from benign tissues. These handcrafted descriptors are fused with high-level features extracted using convolutional neural networks (CNNs) to construct a comprehensive feature representation. Experimental evaluation on publicly available prostate imaging datasets demonstrates that the proposed fusion framework significantly outperforms single-feature and traditional methods, achieving a classification accuracy of 97.32%. The results highlight the effectiveness of combining mathematical modeling, radiomics, and artificial intelligence for improved prostate cancer diagnosis.

It is a well-known fact that studying entropies in physiological systems, like the cardiovascular system, to quantify dynamic complexity is a well-established field that aids in autonomous health condition evaluation, reducing the need for continuous expert monitoring. Pathological cardiac conditions such as Congestive Heart Failure (CHF) patients and Atrial Fibrillation (AFib) often pose a risk of co-occurrence, which can be lethal. The primary goal of this study is to collectively analyse the symbolic dynamics of human heart rhythms obtained from normal (healthy), CHF, and AFib subjects, using Normalised Corrected Shannon Entropy (NCSE). This study performs joint differentiation of CHF and AFib conditions, instead of performing individual detection as done in previous studies. Initially, the RR time series data, both filtered and raw, from ECGs of 15 subjects are symbolised and converted into series of codes. These code series are further evaluated to compute absolute and mean NCSE values across different thresholds and word lengths. In all the scenarios, the NCSE outcomes showed significant differences between normal and diseased cardiovascular systems, with AFib exhibiting the highest entropy, followed by normal and CHF systems. The results of NCSE and Shannon entropy (SE) were compared and it was observed that NCSE demonstrated superior performance in separating the three conditions, as well as in executing joint detection. The NCSE results have also been statistically analysed and validated using surrogate data analysis, one-way ANOVA and pairwise t-tests. Furthermore, this approach did not require any classification algorithms or noise cancellation methods, indicating robustness to noise.

In recent years, the science of ophthalmology has seen substantial developments, with an increasing demand for precise and comprehensive diagnosis. Traditional detection approaches sometimes fail to reflect the complicated interactions between diverse eye disorders. To tackle this issue, we present “Visionary Neural Networks,” an innovative approach that uses the power of graph-based architectures to give a comprehensive solution for ocular disease analysis, such as cataract, diabetic retinopathy, glaucoma, and normal eyes. The proposed approach incorporates detailed features essential for disease identification, employing the intrinsic spatial relationships in retinal images. For each retinal image, the model builds a graph representation in which the pixels are the nodes and the edges are the spatial relationships between them. Through node-to-node communication and the use of graph-like neural networks in our design, the model dynamically learns context-aware features. In order to allow the architecture to carefully emphasise pertinent characteristics during feature transmission, the model is equipped to adapt the graph-based representation of the image. The proposed architecture outperforms traditional approaches, achieving an accuracy of over 76% and an AUC-ROC of 0.99. This research enhances medical diagnosis and patient care in ophthalmology by offering a meaningful methodological contribution to the 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.

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.

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.