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Prediction framework for upper body sedentary working behaviour by using deep learning and machine learning

Rama Krishna Reddy Guduru1, Aurelijus Domeika1, Milda Dubosiene1

  • 1Institute of Mechatronics, Kaunas University of Technology, Kaunas, Lithuania.

Soft Computing
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep neural network (DNN) method to detect poor posture during sedentary work, providing real-time feedback to prevent health issues. The system achieved 97.2% accuracy in identifying posture changes.

Keywords:
DNNKNNMATLAB softwareRandom forestSVMSedentary behaviour

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Public Health

Background:

  • Sedentary activity is a growing public health concern linked to various diseases.
  • Identifying and correcting poor posture during prolonged sitting is crucial for mitigating health risks.
  • Existing methods for posture analysis lack the accuracy and real-time feedback capabilities needed for effective intervention.

Purpose of the Study:

  • To propose and validate a novel method for identifying upper body posture changes during sedentary work.
  • To develop a feedback system that alerts individuals to correct their posture in real-time.
  • To compare the performance of the proposed deep neural network (DNN) method against traditional machine learning algorithms.

Main Methods:

  • Human pose estimation was performed on captured images.
  • Image pre-processing involved bandpass filtering and morphological operations (dilation, erosion, opening).
  • Texture feature extraction and deep neural network (DNN) models were employed for posture prediction and analysis in MATLAB.

Main Results:

  • The DNN-based method achieved high accuracy (97.2%), sensitivity (88.7%), and specificity (99.1%) in posture prediction.
  • Performance significantly surpassed existing methods like Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN).
  • A feedback system using an alarm was developed to alert users to posture deviations.

Conclusions:

  • The proposed DNN method offers a highly accurate and effective solution for real-time posture monitoring during sedentary work.
  • This technology has the potential to prevent cardiovascular and musculoskeletal diseases associated with prolonged sedentary behavior.
  • The system provides a valuable tool for public health initiatives aimed at reducing the negative impacts of sedentary lifestyles.