Data envelopment analysis (DEA) for improving ergonomics and workplace performance

The purpose of the study was to develop a framework utilizing the Constant Returns to Scale (CCR) model of Data Envelopment Analysis (DEA) to evaluate the performance of workers and ergonomic risk and identify their postural models from efficient frontiers. Surface Electromyography (EMG) data and upper limb joint angle data were collected from volunteers (Decision-Making Units (DMUs) to carry out the DEA analysis. The data was collected for both maximum voluntary isometric contractions (MVC) and simple dynamic exercises. The DEA analysis was performed in several phases, including problem formulation and Single-Input-Multiple-Output (SIMO) model analysis. The study used muscle activation levels and upper limb joint angles to evaluate the ergonomic risks and performance of workers and identify role models for typical workers to follow. The study found that incorporating kinematics and EMG data into the DEA model's CCR framework identified efficient frontiers for workers who exhibit less muscle activation and use optimal arm angles while performing their work. The study also showed that workers can learn from their role models who exhibit efficient techniques, including the appropriate arm angle for performing a particular task, to improve their own efficiency. By following these superior work procedures, workers can increase their efficiency, reduce the risk of musculoskeletal problems, and enhance their output. The study concluded that the DEA framework utilizing the CCR model, combined with kinematics and EMG data, can assist in determining the performance of workers and best practices for workers to improve their performance and reduce ergonomic risk.