Exercise & Fitness

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscap...

Optimizing postprandial glucose prediction through integration of diet and exercise: Leveraging transfer learning with imbalanced patient data.

BACKGROUND: In recent years, numerous methods have been introduced to predict glucose levels using machine-learning techniques on patients' daily behavioral and continuous glucose data. Nevertheless, a definitive consensus remains elusive regarding modeling the combined effects of diet and exercise for optimal glucose prediction. A notable challenge is the propensity for observational patient data...

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.

AIM: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improv...

Tai Chi Expertise Classification in Older Adults Using Wrist Wearables and Machine Learning.

Tai Chi is a Chinese martial art that provides an adaptive and accessible exercise for older adults with varying functional capacity. While Tai Chi is...

Neural network extrapolation to distant regions of the protein fitness landscape.

Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the disco...

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly ...

Additional Rehabilitative Robot-Assisted Gait Training for Ambulation in Geriatric Individuals with Guillain-Barré Syndrome: A Case Report.

We present a case of a 75-year-old Asian woman with Guillain-Barré syndrome (GBS) who underwent a 1-month comprehensive rehabilitation training progra...

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

BACKGROUND: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ sta...

Leveraging camera traps and artificial intelligence to explore thermoregulation behaviour.

Behavioural thermoregulation has critical ecological and physiological consequences that profoundly influence individual fitness and species distribut...

Constructing prediction models and analyzing factors in suicidal ideation using machine learning, focusing on the older population.

Suicide among the older population is a significant public health concern in South Korea. As the older individuals have long considered suicide before...

Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.

Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to per...

Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training.

Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be sol...

Machine Learning for Movement Pattern Changes during Kinect-Based Mixed Reality Exercise Programs in Women with Possible Sarcopenia: Pilot Study.

BACKGROUND: Sarcopenia is a muscle-wasting condition that affects older individuals. It can lead to changes in movement patterns, which can increase t...

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare.

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning se...

The Application of Robotics in Cardiac Rehabilitation: A Systematic Review.

: Robotics is commonly used in the rehabilitation of neuro-musculoskeletal injuries and diseases. While in these conditions, robotics has clear benefi...

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, ...

Effects of precise cardio sounds on the success rate of phonocardiography.

This work investigates whether inclusion of the low-frequency components of heart sounds can increase the accuracy, sensitivity and specificity of dia...

Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.

The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical hea...

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

PURPOSE: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent perform...

Predicting long-term sleep deprivation using wearable sensors and health surveys.

Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have significant negative impacts on our attention, cognit...