Workload Intensity in Human-Robot Collaboration: A Personalized Approach to Sustainable Efficiency and Well-Being

ObjectiveTo investigate how varying workload intensity and personalized conditions influence physiological stress responses and task efficiency in human-robot collaboration (HRC).BackgroundHRC is increasingly used to enhance productivity and reduce physical demands, yet workers' mental and physiological strain remains unaddressed. High workload intensity, often induced by robot pacing, can elevate autonomic stress responses, and current systems rarely adapt workload to individual worker capacity. Understanding how workload levels, particularly personalized pacing, affect physiological stress and task performance is essential for designing human-centric collaborative workplaces.MethodsThe study consisted of two experiments. In the first experiment (E1), participants completed an assembly task with a collaborative robot (CR) under two workload conditions (low, high). In the second experiment (E2), participants completed an additional personalized scenario in which the CR motion parameters were adjusted to each participant's capacity. Heart rate variability (HRV) indicators were assessed, and task performance was evaluated using assembly time (AT).ResultsIn E1, the high workload reduced AT, but increased the heart rate (HR) and decreased the root mean square of successive differences (RMSSD), indicating elevated stress. In E2, the personalized scenario maintained efficiency while lowering the HR and restoring the RMSSD compared to the high-intensity condition. The high-frequency (HF) power showed no significant variation in either experiment.ConclusionsWorkload intensity significantly affects both efficiency and physiological stress in HRC. Personalizing the CR motion parameters to worker capacity preserves performance while reducing stress responses.ApplicationAdaptive, capacity-based HRC strategies can help manufacturers sustain productivity without compromising worker well-being.