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An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning

Jehangir Arshad1, Hafiza Mahnoor Asim1, Muhammad Adil Ashraf1

  • 1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan.

Computational Intelligence and Neuroscience
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Summary

This study introduces a cost-efficient system using machine learning to detect improper sitting postures, achieving 99.8% accuracy. The technology helps prevent health issues from prolonged poor posture for desk workers.

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Ergonomics

Background:

  • Prolonged improper sitting posture leads to significant health issues, including pain and potentially fatal conditions.
  • The need for effective, accessible solutions to monitor and correct posture in occupational settings is critical.

Purpose of the Study:

  • To develop an intelligent and cost-efficient system for detecting improper sitting postures using machine learning.
  • To compare the effectiveness of two sensor arrangements for posture detection.

Main Methods:

  • Machine learning classification algorithms including K-nearest neighbor (KNN), Naive Bayes, logistic regression, and random forest were employed.
  • Two system arrangements were evaluated: one with six force-sensitive resistor (FSR) sensors, and another with two FSR sensors and one ultrasonic sensor.

Main Results:

  • An accuracy of 99.8% was achieved in detecting improper postures (backward-leaning, forward-leaning, left-leaning, right-leaning).
  • The system utilizing fewer sensors demonstrated superior cost-efficiency with high accuracy and reduced execution time.

Conclusions:

  • The proposed system offers a highly accurate and cost-effective solution for monitoring sitting posture.
  • This technology is essential for office workers and remote employees to mitigate the adverse health effects of prolonged sitting.