AIMC Topic: Sleep
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Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics.
Biosensors & bioelectronics
Mar 17, 2025
Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthcare. Detecting fatigue, stress, and sleep is particularly important due to their interdependence: pe...
FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in .
Science advances
Mar 12, 2025
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVI...
A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.
Proceedings of the National Academy of Sciences of the United States of America
Feb 11, 2025
In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin cou...
Human sleep position classification using a lightweight model and acceleration data.
Sleep & breathing = Schlaf & Atmung
Feb 10, 2025
PURPOSE: This exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux dise...
Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.
BioMed research international
Feb 10, 2025
Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time-consuming and prone to bias. This study was aimed at...
Subjective recovery in professional soccer players: A machine learning and mediation approach.
Journal of sports sciences
Feb 5, 2025
Coaches often ask players to judge their recovery status (subjective recovery). We aimed to explore potential determinants of subjective recovery in 101 male professional soccer players of 4 Italian Serie C teams and to further investigate whether th...
Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions.
Computers in biology and medicine
Jan 30, 2025
The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, ...
Ultrasound Predicts Drug-Induced Sleep Endoscopy Findings Using Machine Learning Models.
The Laryngoscope
Jan 29, 2025
OBJECTIVES: Ultrasound is a promising low-risk imaging modality that can provide objective airway measurements that may circumvent limitations of drug-induced sleep endoscopy (DISE). This study was devised to identify ultrasound-derived anatomical me...
Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors.
Sensors (Basel, Switzerland)
Jan 14, 2025
Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position...
Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.
Sleep health
Jan 9, 2025
GOAL AND AIMS: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a m...
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