Fibromyalgia

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial ne...

The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review.

Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML - eye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equip...

Adaptive Multimodel Knowledge Transfer Matrix Machine for EEG Classification.

The emerging matrix learning methods have achieved promising performances in electroencephalogram (EEG) classification by exploiting the structural in...

Machine learning model with output correction: Towards reliable bradycardia detection in neonates.

Bradycardia is a commonly occurring condition in premature infants, often causing serious consequences and cardiovascular complications. Reliable and ...

Interpretable machine learning for evaluating risk factors of freeway crash severity.

Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is cru...

Machine learning identifies risk factors associated with long-term opioid use in fibromyalgia patients newly initiated on an opioid.

OBJECTIVES: Fibromyalgia is frequently treated with opioids due to limited therapeutic options. Long-term opioid use is associated with several advers...

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index.

BACKGROUND: Physical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, am...

Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this stud...

Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.

There are significant challenges to identifying which individuals require intervention following exposure to trauma, and a need for strategies to iden...

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning.

PURPOSE: Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accur...

A Mouth and Tongue Interactive Device to Control Wearable Robotic Limbs in Tasks where Human Limbs Are Occupied.

The Wearable Robotic Limb (WRL) is a type of robotic arm worn on the human body, aiming to enhance the wearer's operational capabilities. However, pro...

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms ma...

Robotic assisted and exoskeleton gait training effect in mental health and fatigue of multiple sclerosis patients. A systematic review and a meta-analysis.

PURPOSE: Robotic and Exoskeleton Assisted Gait Training (REAGT) has become the mainstream gait training module. Studies are investigating the psychoso...

Elevator block brake structural optimization design based on an approximate model.

An Aquila optimizer-back propagation (AO-BP) neural network was used to establish an approximate model of the relationship between the design variable...

Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.

INTRODUCTION: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reduci...

The scope of artificial intelligence in retinopathy of prematurity (ROP) management.

Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the de...

Cognitive ergonomics and robotic surgery.

Cognitive ergonomics refer to mental resources and is associated with memory, sensory motor response, and perception. Cognitive workload (CWL) involve...

Modeling Fatigue in Manual and Robot-Assisted Work for Operator 5.0.

A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.

In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. How...

A generative adaptive convolutional neural network with attention mechanism for driver fatigue detection with class-imbalanced and insufficient data.

Over the past few years, fatigue driving has emerged as one of the main causes of traffic accidents, necessitating the development of driver fatigue d...