Ergonomic risk assessment for supporting surgeons’ well-being using CNN and dragonfly optimization

Surgeons face many musculoskeletal problems mainly because they have to take up awkward positions and carry out repetitive motions for long periods. These issues not only pose risks to the health of the surgeon but also affect the quality of patient care adversely. The usual methods for identifying ergonomic risks, which require the filling out of questionnaires or surveys, are not usually very effective. The traditional methods for evaluating ergonomic hazards via the use of questionnaires and surveys are often ineffective. In this regard, the present study takes a different route by creating an intelligent technology that automatically evaluates ergonomic risks through the continuous observation of surgeons’ movements and postures during surgical operations. The method being discussed relies chiefly on a deep learning based CNN (Convolutional Neural Network), which extracts the most important features from visual data in a manner analogous to a continuous monitoring system. The system’s intelligence is further enhanced by the optimization of the CNN via the Dragonfly Optimization Algorithm (DOA). DOA efficiently investigates new options for the optimization of the most promising ones, thus enhancing the model’s learning capacity and addressing key challenges such as overfitting and inefficiency. All the components were created and tested in MATLAB, taking a publicly accessible dataset of surgical postures. The integration of CNN-DOA methodology proposed in this paper yields a classification accuracy of 97.56%. However, it also outperformed various conventional machine learning models, not only in terms of accuracy but also in precision, recall, and computational efficiency. This method could empower the surgeons to recognize and tackle ergonomic problems effectively before they turn into more serious issues.