Exercise & Fitness

The factors affecting aerobics athletes' performance using artificial intelligence neural networks with sports nutrition assistance.

This work aims to comprehensively explore the influencing factors of aerobics athletes' performance by integrating sports nutrition assistance and artificial intelligence neural networks. First, a personalized assessment and analysis of athletes' nutritional needs are conducted, collecting various data including fitness tests, physiological monitoring, and surveys to establish a personalized nutri...

Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.

Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and differe...

Machine learning insights into scapular stabilization for alleviating shoulder pain in college students.

Non-specific shoulder pain is a common musculoskeletal condition, especially among college students, and it can have a negative impact on the patient'...

Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study.

Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and...

Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.

Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and exter...

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.

Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in imple...

Circulating endothelial progenitor cells and inflammatory markers in type 1 diabetes after an acute session of aerobic exercise.

OBJECTIVE: To determine circulating endothelial progenitor cells (EPC) counts and levels of inflammatory markers in individuals with and without type ...

Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models.

Nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conve...

Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate.

In clinical practice, the glomerular filtration rate (GFR), a measurement of kidney functioning, is normally calculated using equations, such as the E...

Review of deep representation learning techniques for brain-computer interfaces.

In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) si...

Multi-Activity Step Counting Algorithm Using Deep Learning Foot Flat Detection with an IMU Inside the Sole of a Shoe.

Step counting devices were previously shown to be efficient in a variety of applications such as athletic training or patient's care programs. Various...

Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

BACKGROUND: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T...

Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery.

The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-prote...

Intelligent wearable-assisted digital healthcare industry 5.0.

The latest evolution of the healthcare industry from Industry 1.0 to 5.0, incorporating smart wearable devices and digital technologies, has revolutio...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based an...

OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation.

Several studies report the benefits and accuracy of using autosegmentation for organ at risk (OAR) outlining in radiotherapy treatment planning. Typic...

Boolean Computation in Single-Transistor Neuron.

Brain neurons exhibit far more sophisticated and powerful information-processing capabilities than the simple integrators commonly modeled in neuromor...

Non-specific myocardial fibrosis in young competitive athletes: clinical significance and risk prediction by a powerful machine learning-based model.

BACKGROUND: Non-specific myocardial fibrosis (NSMF) is a heterogeneous entity. We aimed to evaluate young athletes with and without NSMF to establish ...

Non-invasive brain-machine interface control with artificial intelligence copilots.

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the la...

Modeling health risks using neural network ensembles.

This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a he...