Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Understanding Sleep01:11

Understanding Sleep

Sleep, an essential biological state, involves significant reductions in physical activity, sensory awareness, and interaction with the environment. This complex physiological process is primarily regulated by specific brain regions, notably the hypothalamus and pons, which govern the sleep-wake cycle or circadian rhythm.
The circadian rhythm, a nearly 24-hour cycle, is deeply influenced by environmental light cues. Light exposure directly affects the hypothalamus, which in turn regulates...
Sleep Apnea01:21

Sleep Apnea

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Two-Step Sensor Fusion Methodology to Assess Damage on Drone Propellers by Audio and Radar Measurements.

Sensors (Basel, Switzerland)·2026
Same author

Piezoelectric Sensors as Energy Harvesters for Ultra Low-Power IoT Applications.

Sensors (Basel, Switzerland)·2024
Same author

Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging.

Physiological measurement·2024
Same author

LoRaWAN for Vehicular Networking: Field Tests for Vehicle-to-Roadside Communication.

Sensors (Basel, Switzerland)·2024
Same author

LoRaWAN Transmissions in Salt Water for Superficial Marine Sensor Networking: Laboratory and Field Tests.

Sensors (Basel, Switzerland)·2023
Same author

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jun 21, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors.

Giacomo Peruzzi1, Alessandra Galli2, Giada Giorgi1

  • 1Department of Information Engineering, University of Padova, 35122 Padova, Italy.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary

Accurate sleep posture detection is crucial for Obstructive Sleep Apnea (OSA) management. This study introduces a lightweight, microcontroller-based system using embedded machine learning and pressure sensors for precise, real-time sleep position classification.

Keywords:
artificial intelligenceembedded machine learninginternet of thingsobstructive sleep apneapressure sensorssensor selectionsleep posture recognitionsupport vector machine

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

428

Related Experiment Videos

Last Updated: Jun 21, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

428

Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Machine Learning Applications

Background:

  • Sleep posture significantly impacts sleep quality, particularly for Obstructive Sleep Apnea (OSA) patients, affecting breathing patterns.
  • Current automatic sleep posture detection methods (wearable and non-wearable) present limitations like intrusiveness, privacy concerns, cost, and computational demands.

Purpose of the Study:

  • To develop an accurate, lightweight, and privacy-preserving automatic sleep posture detection system.
  • To leverage embedded machine learning on a microcontroller for real-time sleep position classification.

Main Methods:

  • Implementation of a microcontroller-based system utilizing a minimal set of pressure sensors.
  • Execution of an embedded machine learning model for on-device posture classification, processing sensor data locally.
  • Evaluation of system performance with 6 and 15 pressure sensors.

Main Results:

  • High classification accuracy achieved: 0.90 with 6 sensors and 0.96 with 15 sensors.
  • The proposed system demonstrates reduced hardware and computational requirements compared to existing solutions.
  • Local data processing enhances suitability for real-time applications and preserves user privacy.

Conclusions:

  • The microcontroller-based embedded ML approach offers an effective solution for accurate and efficient sleep posture detection.
  • This system provides a viable alternative to current methods, addressing limitations related to intrusiveness, privacy, and complexity.
  • The technology is well-suited for real-time sleep quality assessment and OSA management applications.