Predicting ergonomic risk among laboratory technicians using a Cheetah Optimizer-Integrated Deep Convolutional Neural Network.

Journal: Computers in biology and medicine
PMID:

Abstract

Medical laboratory technicians play a significant role in clinical units by conducting diagnostic tests and analyses. However, their job nature involving repetitive motions, prolonged standing or sitting, etc., leads to potential ergonomic risks. This research proposed a novel hybrid strategy by integrating the Cheetah Optimizer into the Deep Convolutional Neural Network (CHObDCNN) for predicting ergonomic risks in medical laboratory technicians. The presented framework commences with collecting images containing different postures and motions of laboratory technicians working in clinical units. The collected database was pre-processed to eliminate noises and other unwanted features. The DCNN component in the proposed framework performs the ergonomic risk prediction task by examining the patterns and interconnection with the image data, while the CHO component optimizes the DCNN training by tuning its parameters to its optimal range. Thus, the combined methodology offers improved classification results by iteratively updating its parameters. The presented framework was implemented in MATLAB, and the experimental outcomes manifest that the proposed method acquired improved accuracy of 98.74 %, greater precision of 98.56 %, and reduced computational time of 2.45 ms. Finally, the comparative study with the existing techniques validates its effectiveness in ergonomic risk prediction.

Authors

  • Abdulmajeed Azyabi
    Industrial Engineering Department, College of Engineering & Computer Sciences, Jazan University, Jazan, Saudi Arabia.
  • Abdulrahman Khamaj
    Industrial Engineering Department, College of Engineering & Computer Sciences, Jazan University, Jazan, Saudi Arabia.
  • Abdulelah M Ali
    Industrial Engineering Department, College of Engineering & Computer Sciences, Jazan University, Jazan, Saudi Arabia.
  • Mastoor M Abushaega
    Industrial Engineering Department, College of Engineering & Computer Sciences, Jazan University, Jazan, Saudi Arabia.
  • Emad Ghandourah
    Nuclear Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Md Moddassir Alam
    Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin, 39524, Saudi Arabia.
  • Mohammad Tauheed Ahmad
    College of Medicine, King Khalid University, Abha, Saudi Arabia. Electronic address: moahmad@kku.edu.sa.