In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability.

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability, as they are trained on limited lab-controlled data. In this study, we isolated and analyzed the effects of the subject, device, position, and orientation variabilities on DL HAR models using the HARVAR and REALDISP datasets.

A Reading Range- and Frequency-Reconfigurable Antenna for Near-Field and Far-Field UHF RFID Applications.

In radio frequency identification (RFID), differences in spectrum policies and tag misreading in different countries are the two main issues that limit its application. To solve these problems, this article proposes a composite right/left-handed transmission line (CRLH-TL)-based reconfigurable antenna for ultra-high frequency near-field and far-field RFID reader applications. The CRLH-TL is achieved using a periodically capacitive gap-loaded parallel plate line.

Nighttime Cough Characteristics in Chronic Obstructive Pulmonary Disease Patients.

Coughing is a symptom of many respiratory diseases. An increased amount of coughs may signal an (upcoming) health issue, while a decreasing amount of coughs may indicate an improved health status. The presence of a cough can be identified by a cough classifier.

Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles.

Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, which result in the collection of redundant information, which can deteriorate performance while also increasing the associated network costs.

Evaluating the Performance Characteristics of Pressure Monitoring Systems.

Measuring interface pressure is currently used in a variety of settings, e.g., automotive or clinical, to evaluate pressure distribution at support surface interfaces.

Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination.

With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of fatigue can optimize training, prevent excessive fatigue and resultant injury, and increase efficiency and safety.

Energy-Efficient Route Planning Method for Ships Based on Level Set.

To reduce the fuel consumption of ships' oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces the influence factor of ocean current to optimize routing in dynamically changing marie environments. First, models for the energy consumption of ships and flow fields are established.

Analysis of the Effect of Skin Pigmentation and Oxygen Saturation on Monte Carlo-Simulated Reflectance Photoplethysmography Signals.

The effect of skin pigmentation on photoplethysmography and, specifically, pulse oximetry has recently received a significant amount of attention amongst researchers, especially since the COVID-19 pandemic. With most computational studies observing overestimation of arterial oxygen saturation (SpO) in individuals with darker skin, this study seeks to further investigate the root causes of these discrepancies. This study analysed intensity changes from Monte Carlo-simulated reflectance PPG signals across light, moderate, and dark skin types at oxygen saturations of 70% and 100% in MATLAB R2024a.

Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach.

Objective and continuous monitoring of Parkinson's disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labeled training data, we propose to use prototypical networks, which can embed domain expertise about the heterogeneous tremor and non-tremor sub-classes. We evaluated our approach using data from the Parkinson@Home Validation study, including 8 PD patients with tremor, 16 PD patients without tremor, and 24 age-matched controls.

Embedding Trust in the Media Access Control Protocol for Wireless Networks.

IEEE 802.11 is one of the most common medium access control (MAC) protocols used in wireless networks. The carrier sense multiple access with collision avoidance (CSMA/CA) mechanisms in 802.

Elephant Sound Classification Using Deep Learning Optimization.

Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation. This study addresses elephant sound classification utilizing raw audio processing.

Enhancing Low-Light Images with Kolmogorov-Arnold Networks in Transformer Attention.

Low-light image enhancement (LLIE) techniques improve the performance of image sensors by enhancing visibility and details in poorly lit environments and have significantly benefited from recent research into Transformer models. This work presents a novel Transformer attention mechanism inspired by the Kolmogorov-Arnold representation theorem, incorporating learnable non-linearity and multivariate function decomposition. This innovative mechanism is the foundation of KAN-T, our proposed Transformer network.

Validation and Analysis of Recreational Runners' Kinematics Obtained from a Sacral IMU.

Our aim was to validate a sacral-mounted inertial measurement unit (IMU) for reconstructing running kinematics and comparing movement patterns within and between runners. IMU data were processed using Kalman and complementary filters separately. RMSE and Bland-Altman analysis assessed the validity of each filtering method against a motion capture system.

Inductive Power Transfer Coil Misalignment Perception and Correction for Wirelessly Recharging Underground Sensors.

Field implementations of fully underground sensor networks face many practical challenges that have limited their overall adoption. Power management is a commonly cited issue, as operators are required to either repeatedly excavate batteries for recharging or develop complex underground power infrastructures. Prior works have proposed wireless inductive power transfer (IPT) as a potential solution to these power management issues, but misalignment is a persistent issue in IPT systems, particularly in applications involving moving vehicles or obscured (e.

Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.

Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.

Alcohol, Tobacco and Illicit Drug Use During Pregnancy in the Longitudinal BELpREG Cohort in Belgium Between 2022 and 2024.

Substance use during pregnancy is associated with adverse outcomes for both mother and child. This study aimed to determine the prevalence and determinants of alcohol, tobacco and illicit drug use before and during pregnancy in Belgium. An observational study was conducted using data from the longitudinal BELpREG registry.

TMJ Replacement in Degenerative Disease: A Systematic Review.

: This study aims to describe and analyze the indications and clinical results of total TMJ replacement in participants with degenerative and/or inflammatory joint diseases, defining patient and intervention conditions. : A systematic review was conducted according to the Cochrane Handbook for Systematic Reviews of Intervention and reported according to the PRISMA Items update. The search strategy was from 1997 to July 2024 in Pubmed, Embase, Scopus, and Web of Science.

Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology.

Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance in gastroenterology remains untested. This study assesses ChatGPT-4's performance in interpreting gastroenterology images.

Evaluation of Perineal Descent Measurements on Pelvic Floor Imaging.

The aim of this study is to validate a uniform method for measuring perineal descent which can be used for different imaging methods, to establish cut-off values for this measurement, and to assess diagnostic test accuracy (DTA) of imaging techniques using these cut-off values. Secondly, the study aims to correlate perineal descent to symptoms, signs and imaging findings in women with obstructed defaecation syndrome (ODS) to assess its clinical relevance. Cross-sectional study of 131 women with symptoms of ODS.

The External Validation of a Multivariable Prediction Model for Recurrent Pelvic Organ Prolapse After Native Tissue Repair: A Prospective Cohort Study.

: A prediction model for anatomical cystocele recurrence after native tissue repair was developed and internally validated in 2016. This model estimates a patients' individual risk of recurrence and can be used for counseling. Before implementation in urogynecological clinical practice, external validation is needed.

Infections: Bridging Clinical Observations and Epidemiological Insights.

Purpose: is the main etiologic agent implicated in primary amoebic meningoencephalitis (PAM). It is also known as the brain-eating amoeba because of the severe brain inflammation following infection, with a survival rate of about 5%. This review aims to identify infections and evaluate patients' progression.

Post COVID-19 and Long COVID Symptoms in Otorhinolaryngology-A Narrative Review.

Post/Long COVID (syndrome) is defined as a condition with symptoms persisting for more than 12 weeks after the onset of SARS-CoV-2 infection that cannot be explained otherwise. The prevalence of self-reported otorhinolaryngological Post/Long COVID symptoms is high. The aim of this review was to analyze the current literature regarding the actual prevalence, knowledge of the etiopathology, and evidence-based treatment recommendations of otorhinolaryngology-related Post/Long COVID symptoms.

Efficacy and Safety of Pericapsular Nerve Group Block (PENG) in Hip Surgery Under General Anaesthesia: A Systematic Literature Review and Meta-Analysis.

: The pericapsular nerve group (PENG) block is a novel ultrasound-guided regional technique that may provide analgesia to patients undergoing hip surgery. It has been extensively studied in recent years, but the evidence of superiority over other regional anaesthetic techniques is inconclusive. This review aimed to compare outcomes of the PENG block in patients undergoing hip surgery with standard techniques under general anaesthesia.

How Reproducible Are the Ultrasound Features of Adenomyosis Defined by the Revised MUSA Consensus?

: The aim of this study is to assess the inter- and intra-observer reproducibility of the identification of direct and indirect ultrasonographic features of adenomyosis as defined by the revised Morphological Uterus Sonographic Assessment (MUSA) consensus (2022). : A cohort of 74 women, aged 18 to 45, were recruited from the recurrent miscarriage and general gynaecology clinic at a university-based fertility centre. All the participants underwent 2D and 3D transvaginal Ultrasound scan (TVS) examination in the late follicular and early luteal phase.

An Investigation into the Pathomechanism and Prognostic Markers for Predicting the Outcome of Patients with Moderate and Severe Aortic Stenosis with or Without Intervention.

Our understanding of the impact of aortic valve stenosis (AS) on patient outcomes irrespective of its severity has changed since the advent of populational databases on AS following the landmark publication by Ross and Braunwald in 1968 [...