Electromyography and Inertial Measurement Unit Analysis for Assessing Ergonomic Risk and Biomechanical Efficiency

Abstract

This study develops a framework for assessing ergonomic risk by integrating electromyography (EMG), inertial measurement unit (IMU) data, and subjective task evaluations. EMG quantifies muscle activation and fatigue using root mean square and power spectral density metrics, while IMU data provide joint kinematics through accelerometer and gyroscope integration. Ninety-four participants performed standardized tasks in three categories: assembly, culinary, and ladder-related work. EMG sensors targeted task-relevant muscles, and IMUs captured movement dynamics. Statistical analysis, including Pearson correlation and Bayesian hierarchical modeling, evaluated the relationship between neuromuscular strain, movement efficiency, and perceived ergonomic risk. Preliminary findings indicate that physically demanding tasks are associated with higher EMG activation and perceived discomfort. IMU-based kinematics yielded reliable joint motion estimates across tasks. Bayesian modeling revealed the influence of expertise and demographic variables on ergonomic perception. This method evaluates biomechanical efficiency and ergonomic risk for a better understanding of task-specific physical demands in occupational settings.

Keywords

biomechanics engineering measures modeling & analytics performance

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