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

You might also read

Related Articles

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

Sort by
Same author

Double trouble! Concomitant distal ulna fractures predict worse 1-year outcome in distal radius fractures: a registry-based cohort study of 5,536 patients.

Acta orthopaedica·2025
Same author

Advanced Necklace for Real-Time PPG Monitoring in Drivers.

Sensors (Basel, Switzerland)·2024
Same author

Patients Aged 80 or More With Distal Radius Fractures Have a Lower One-Year Mortality Rate Than Age- and Gender-Matched Controls: A Register-Based Study.

Geriatric orthopaedic surgery & rehabilitation·2024
Same author

Enhancing COVID-19 Detection: An Xception-Based Model with Advanced Transfer Learning from X-ray Thorax Images.

Journal of imaging·2024
Same author

Clinical and Patient-Reported Outcomes After Total Wrist Arthroplasty and Total Wrist Fusion: A Prospective Cohort Study with 2-Year Follow-up.

JB & JS open access·2024
Same author

Drivers' Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Sep 3, 2025

A Vibrotactile Feedback Device for Seated Balance Assessment and Training
09:13

A Vibrotactile Feedback Device for Seated Balance Assessment and Training

Published on: January 20, 2019

6.5K

Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms.

Taraneh Aminosharieh Najafi1, Antonio Abramo1, Kyandoghere Kyamakya2

  • 1Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.

Sensors (Basel, Switzerland)
|July 28, 2022
PubMed
Summary

This study introduces a smart chair sensor system to identify sitting postures, crucial for mitigating health risks from sedentary lifestyles. An echo memory network achieved 91.68% accuracy in posture classification.

Keywords:
deep learning modelspressure sensorssitting postures classificationsmart chair

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

4.0K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

Related Experiment Videos

Last Updated: Sep 3, 2025

A Vibrotactile Feedback Device for Seated Balance Assessment and Training
09:13

A Vibrotactile Feedback Device for Seated Balance Assessment and Training

Published on: January 20, 2019

6.5K
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

4.0K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Modern lifestyles often involve prolonged sedentary behavior, increasing the risk of health issues due to poor sitting postures.
  • Existing solutions lack the ability to accurately and unobtrusively monitor sitting postures in real-time.
  • Developing smart environments requires intelligent systems capable of understanding user behavior and physical state.

Purpose of the Study:

  • To design, develop, and evaluate a smart chair sensor system for accurate sitting posture identification.
  • To assess the effectiveness of deep learning algorithms for classifying various sitting postures.
  • To create a low-cost, versatile system deployable in diverse smart environments.

Main Methods:

  • Integration of eight pressure sensors into a chair's cushion and backrest.
  • Development of a custom signal acquisition board for data collection and Wi-Fi transmission.
  • Utilizing a graphical user interface for real-time monitoring and data storage.
  • Conducting an experiment with 40 subjects to collect sitting posture data.
  • Evaluating seven deep learning algorithms for posture classification.

Main Results:

  • The smart chair system successfully acquired and transmitted sensor data wirelessly.
  • An echo memory network model demonstrated the highest accuracy at 91.68% for classifying eight distinct sitting postures.
  • The system proved to be simple, versatile, low-cost, and accurate.

Conclusions:

  • The developed smart chair sensor system effectively identifies sitting postures, offering a proactive approach to health management in sedentary populations.
  • Deep learning, particularly echo memory networks, shows significant promise for real-time posture analysis in smart chair applications.
  • This technology has broad applicability in both public and private smart environments to promote healthier sitting habits.