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

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.