Biomedical Engineering Online

PURPOSE: Non-invasive, beat-to-beat variations in physiological indices provide an opportunity for more accessible assessment of autonomic dysfunction. The potential association between the changes in these parameters and arterial stiffness in hypertension remains poorly understood. This systematic review aims to investigate the association between non-invasive indicators of autonomic function based on beat-to-beat cardiovascular signals with arterial stiffness in individuals with hypertension...

BACKGROUND: Adolescent idiopathic scoliosis (AIS), three-dimensional spine deformation, affects body motion. Previous research had indicated pathological gait patterns of AIS. However, the impact of the curve number on the walking mechanism has not been established. Therefore, this study aimed to compare the gait symmetry and kinematics in AIS patients with different curve numbers to healthy control. RESULTS: In the spinal region, double curves AIS patients demonstrated a smaller sagittal symmetry angle (SA) and larger sagittal convex ROM of the trunk and lower spine than the control group...

BACKGROUND: Human activity Recognition (HAR) using smartphone sensors suffers from two major problems: sensor orientation and placement. Sensor orientation and sensor placement problems refer to the variation in sensor signal for a particular activity due to sensors' altering orientation and placement. Extracting orientation and position invariant features from raw sensor signals is a simple solution for tackling these problems. Using few heuristic features rather than numerous time-domain and frequency-domain features offers more simplicity in this approach...

Human-robot walking with prosthetic legs and exoskeletons, especially over complex terrains, such as stairs, remains a significant challenge. Egocentric vision has the unique potential to detect the walking environment prior to physical interactions, which can improve transitions to and from stairs. This motivated us to develop the StairNet initiative to support the development of new deep learning models for visual perception of real-world stair environments. In this study, we present a comprehensive overview of the StairNet initiative and key research to date...

BACKGROUND AND AIM: Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes...

Individuals with incomplete spinal-cord injury/disease are at an increased risk of falling due to their impaired ability to maintain balance. Our research group has developed a closed-loop visual-feedback balance training (VFBT) system coupled with functional electrical stimulation (FES) for rehabilitation of standing balance (FES + VFBT system); however, clinical usage of this system is limited by the use of force plates, which are expensive and not easily accessible. This study aimed to investigate the feasibility of a more affordable and accessible sensor such as a depth camera or pressure mat in place of the force plate...

BACKGROUND: The research gap addressed in this study is the applicability of deep neural network (NN) models on wearable sensor data to recognize different activities performed by patients with Parkinson's Disease (PwPD) and the generalizability of these models to PwPD using labeled healthy data. METHODS: The experiments were carried out utilizing three datasets containing wearable motion sensor readings on common activities of daily living. The collected readings were from two accelerometer sensors...

BACKGROUND: Previous studies have reported that abnormal interlimb coordination is a typical characteristic of motor developmental delay (MDD) during human movement, which can be visually manifested as abnormal motor postures. Clinically, the scale assessments are usually used to evaluate interlimb coordination, but they rely heavily on the subjective judgements of therapists and lack quantitative analysis. In addition, although abnormal interlimb coordination of MDD have been studied, it is still unclear how this abnormality is manifested in physiology-related kinematic features...

Automatic speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. These challenges and others have obscured the ability to distinguish different ALS disease stages/severities...

PURPOSE: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models...

BACKGROUND: Turning in place is a challenging motor task and is used as a brief assessment test of lower limb function and dynamic balance. This review aims to examine how research of instrumented analysis of turning in place is implemented. In addition to reporting the studied population, we covered acquisition systems, turn detection methods, quantitative parameters, and how these parameters are computed. METHODS: Following the development of a rigorous search strategy, the Web of Science and Scopus were systematically searched for studies involving the use of turning-in-place...

BACKGROUND: The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints...

BACKGROUND: Tele-rehabilitation, also known as tele-rehab, uses communication technologies to provide rehabilitation services from a distance. The COVID-19 pandemic has highlighted the importance of tele-rehab, where the in-person visits declined and the demand for remote healthcare rises. Tele-rehab offers enhanced accessibility, convenience, cost-effectiveness, flexibility, care quality, continuity, and communication. However, the current systems are often not able to perform a comprehensive movement analysis...

BACKGROUND: Functional electrical stimulation (FES) can be used in rehabilitation to aid or improve function in people with paralysis. In clinical settings, it is common practice to use transcutaneous electrodes to apply the electrical stimulation, since they are non-invasive, and can be easily applied and repositioned as necessary. However, the current electrode options available for transcutaneous FES are limited and can have practical disadvantages, such as the need for a wet interface with the skin for better comfort and performance...

BACKGROUND: Atherosclerosis may be linked to morphological defects that lead to variances in coronary artery hemodynamics. Few objective strategies exit at present for generalizing morphological phenotypes of coronary arteries in terms of hemodynamics. We used unsupervised clustering (UC) to classify the morphology of the left main coronary artery (LM) and looked at how hemodynamic distribution differed between phenotypes. METHODS: In this study, 76 LMs were obtained from 76 patients...

PURPOSE: Recent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer's disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging. METHODS: The algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data...

Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet...

BACKGROUND: Cycling workload is an essential factor in practical cycling training. Saddle height is the most studied topic in bike fitting, but the results are controversial. This study aims to investigate the effects of workload and saddle height on the activation level and coordination of the lower limb muscles during cycling. METHODS: Eighteen healthy male participants with recreational cycling experience performed 15 × 2-min constant cadence cycling at five saddle heights of 95%, 97%, 100%, 103%, and 105% of greater trochanter height (GTH) and three cycling workloads of 25%, 50%, and 75% of functional threshold power (FTP)...

BACKGROUND: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. METHODS: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way...

BACKGROUND: In this study, an automatic corneal contour extraction algorithm with a shared model is developed to extract contours from dynamic corneal videos containing noise, which improves the accuracy of corneal biomechanical evaluation and clinical diagnoses. The algorithm does not require manual labeling and completes the unsupervised semantic segmentation of each frame in corneal dynamic deformation videos based on a fully convolutional deep-learning network using corneal geometry and texture information...