Influence of operator physical characteristics on compliance with collaborative robot

Abstract

This work investigates the influence of human physical characteristics on behavioural compliance during human–robot collaborative tasks. Using data derived from a collaborative robot experiment published in Behaviour-Based Biometrics for Continuous User Authentication to Industrial Collaborative Robots, participant attributes such as height, gender, and handedness were analyzed against the frequency of non-compliance events. The analysis combined statistical correlation metrics with machine learning-based feature importance estimators to provide both linear and nonlinear perspectives. Pearson, Spearman, and Kendall correlations were computed to quantify monotonic relationships, while model-driven approaches including Random Forest, Gradient Boosting, XGBoost, Mutual Information, and SHAP were used to capture higher-order dependencies. The results show that height exhibits the strongest nonlinear influence on operator compliance, indicating that anthropometric factors substantially affect user behaviour and task adherence. In contrast, gender and handedness were found to contribute moderately, primarily through secondary interaction effects. These findings emphasize the need to account for physical characteristics when designing adaptive and personalized control interfaces for collaborative robots.

Keywords: cognitive load, collaborative robotics, operator characteristics, human-robot collaboration

DOI: 10.54941/ahfe1007117