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Exercise

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

PloS one
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 m...

Acceptance of a mobile telepresence robot used to teach adapted physical activity to isolated older adults: extending and testing the technology acceptance model.

Frontiers in public health
This study aimed to investigate the acceptance of adapted physical activity (APA) by teachers and students before the use of a mobile telepresence robot (MTR), used to remotely supervise isolated older adults' physical activity. While previous studie...

Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students.

Sensors (Basel, Switzerland)
(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of...

Temporal Convolutional Neural Network-Based Prediction of Vascular Health in Elderly Women Using Photoplethysmography-Derived Pulse Wave during Exercise.

Sensors (Basel, Switzerland)
(1) Background: The objective of this study was to predict the vascular health status of elderly women during exercise using pulse wave data and Temporal Convolutional Neural Networks (TCN); (2) Methods: A total of 492 healthy elderly women aged 60-7...

Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step.

Sensors (Basel, Switzerland)
The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably ...

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.

JMIR mHealth and uHealth
BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide diseas...

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

Scientific reports
The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and bio...

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

Computers in biology and medicine
Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have significant negative impacts on our attention, cognition, and mood. The measurement of sleep from in-bed physiological signals has progressed to where co...

Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Sarcopenia is an age-related muscle disease that increases the risk of falls, disabilities, and death. It is associated with increased muscle protein degradation driven by molecular signalling pathways including Akt and FOXO1. This study ...

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairme...