A Multi-Sensor Approach for Cognitive Load Assessment in Mobile Augmented Reality

Augmented reality displays are becoming more powerful and simultaneously more mobile. Although mobile AR is gaining popularity, it remains difficult to get an insight into users' cognitive load, despite its relevance for many mobile-based tasks. Usually, cognitive load is measured via subjective, task-disruptive self-reports such as NASA TLX. While biosensors such as galvanic skin response, heart rate variability, or pulse have been used to obtain more objective measures, these are highly susceptible to motion-induced noise. More robust techniques like EEG offer higher reliability but are impractical for mobile, real-world use. In this paper, we report on a non-contact multi-sensor approach to assess cognitive load in mobile AR. Our approach combines pupillometry, facial expression tracking, and thermal imaging for respiratory rate analysis. Within the frame of our study, we analysed the aptness of the methods, comparing load assessment for low and high cognitive load tasks under both stationary and mobile conditions. Using an XGBoost classifier, our model achieved 86.11% accuracy for binary cognitive load assessment (low vs. high cognitive load) and 84.24% accuracy for four-way classification (cognitive load $\times$ mobility). Feature importance analysis revealed that robust predictors included gaze dynamics (e.g., fixation, pursuit, and saccade durations), pupil diameter metrics (such as FFT band power and variability measures), and facial and respiratory features (including brow lowering and nostril temperature quantiles) for assessing cognitive load in mobile AR.