Fairness in machine learning-based hand load estimation: A case study on load carriage tasks

Abstract

Predicting external hand load from sensor data is essential for ergonomic exposure assessments, as obtaining this information typically requires direct observation or supplementary data. While machine learning can estimate hand load from posture or force data, we found systematic bias tied to biological sex, with predictive disparities worsening in imbalanced training datasets. To address this, we developed a fair predictive model using a Variational Autoencoder with feature disentanglement, which separates sex-agnostic from sex-specific motion features. This enables predictions based only on sex-agnostic patterns. Our proposed algorithm outperformed conventional machine learning models, including $k$-Nearest Neighbors, Support Vector Machine, and Random Forest, achieving a mean absolute error of 3.42 and improving fairness metrics like statistical parity and positive and negative residual differences, even when trained on imbalanced sex datasets. These results underscore the importance of fairness-aware algorithms in avoiding health and safety disadvantages for specific worker groups in the workplace.