Enhancing Adaptive Robotic Coaches with Multimodal Workload Estimation

Social robots are increasingly being explored as interactive coaches capable of delivering personalized physical and cognitive training sessions. Improving their effectiveness entails personalized interventions through adaptive robotic systems with continuous workload quantification. This study presents a workload estimation methodology based on physiological and kinematic monitoring, designed for integration into a social robotic coach. Physical, mental, and dual-task activities were administered to 15 healthy participants, and Support Vector Regression was used to model their perceived workload levels. Physical workload was estimated with a mean absolute error (MAE) of 0.12 +/- 0.01 and a correlation of 0.75 +/- 0.02, demonstrating high reliability across conditions. Mental workload estimation, however, showed greater variability (MAE: 0.18 +/- 0.01, correlation: 0.62 +/- 0.03), particularly in cognitively demanding and high-intensity tasks. This is likely due to overlapping physiological responses to cognitive and physical demands, which introduce ambiguity in signal interpretation. The continuous workload estimation provided by the model can be leveraged to define thresholds offering a discrete interpretation of workload levels.