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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

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Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
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Related Experiment Video

Updated: Dec 18, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
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Millimeter-wave radar based sleep posture transition dataset: SPT.

Jinjun Liu1, Shaoqi Li2, Naji Alhusaini1

  • 1Chuzhou University, 1528 Feng Le Avenue, Chuzhou, Anhui, China.

Data in Brief
|April 11, 2025
PubMed
Summary

This study introduces a new millimeter-wave radar sleep posture transition dataset. This resource aids in developing advanced algorithms for sleep tracking and health monitoring.

Keywords:
Biomedical signal processingHealth informaticsHuman activity recognitionNon-contact measurementRadar sensingSleep monitoring

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Human-Computer Interaction

Background:

  • Millimeter-wave radar offers non-invasive, accurate sleep posture recognition.
  • Existing sleep posture datasets are limited in scale and quality.
  • Contactless sensing is crucial for unobtrusive sleep monitoring.

Purpose of the Study:

  • To introduce a large-scale, high-quality dataset of sleep posture transitions.
  • To facilitate research in sleep monitoring and algorithm development.
  • To support the advancement of millimeter-wave radar applications in healthcare.

Main Methods:

  • Collected data from 20 volunteers (15 male, 5 female) aged 19-25.
  • Utilized a millimeter-wave radar system to capture postural changes.
  • Recorded 1400 samples of seven distinct body position transitions per participant.

Main Results:

  • A comprehensive dataset of millimeter-wave radar signals for sleep posture transitions was created.
  • The dataset captures detailed nocturnal behavioral patterns.
  • Provides a valuable resource for algorithm training and validation.

Conclusions:

  • The proposed dataset addresses the need for extensive sleep posture data.
  • Enables optimization of sleep posture recognition algorithms.
  • Supports advancements in sleep analysis and remote health monitoring.