فهرست نشریات علمی دسترسی آزاد ایران

This study aims to develop a framework for assessing worker performance and ergonomic risks by integrating kinematic data and Electromyography (EMG) into the Multiple-Input-Multiple-Output (MIMO) model of Data Envelopment Analysis (DEA). The focus is on shoulder angles and muscle activation, specifically targeting the Biceps Brachii (BB) and Flexor Carpi Radialis (FCR) muscle groups during dynamic tasks like mopping. The MIMO approach uses kinematic, and EMG data collected from ten subjects performing mopping exercises. The model evaluates Decision-Making Units (DMUs) across multiple inputs and outputs, identifying efficient and inefficient frontiers. Efficiency comparisons were made between the Single-Input-Multiple-Output (SIMO) model, derived from the CCR model, and the MIMO model. The MIMO model revealed efficiency levels ranging from 19% to 100% for the left side and 31.58% to 100% for the right side. DMU-4 was the most efficient on the left arm, and DMUs 3, 4, and 5 on the right arm. The MIMO model provided better distinctions between efficient and inefficient DMUs compared to SIMO. Efficient DMUs, such as DMU-4, serve as role models, showing that better posture and reduced muscle activation in BB and FCR muscles can improve performance and reduce ergonomic risks. The MIMO model offered more efficient assessments of worker performance and ergonomic risks than the traditional SIMO model. Efficient workers with lower muscle activation can improve productivity and reduce ergonomic risks. Future research will investigate other muscle groups, larger sample sizes, and additional dynamic tasks such as lifting to expand its applicability in healthcare.