🤖 AI Summary
Traditional UX research relies heavily on subjective self-reports (e.g., surveys, interviews), limiting its ability to capture implicit user behaviors and cognitive states. To address this, we propose an objective, multimodal physiological assessment framework integrating eye-tracking (Tobii Pro Fusion) with biosignals—including electrodermal activity (EDA), heart rate variability (HRV), and pupil diameter—collected via Empatica E4. We introduce the first systematically validated, interpretable dual-dimensional metric for quantifying cognitive load and engagement, underpinned by an ethics-guided protocol for fusing heterogeneous physiological signals. Leveraging LabStreamingLayer for real-time synchronization and lightweight temporal modeling, our method achieves 91.3% accuracy (F1 = 0.89) in cognitive load classification and reduces engagement prediction error by 37% across 12 real-world interface evaluations. The resulting standardized operating procedure (SOP) for UX evaluation is publicly documented, reproducible, and has been adopted by three leading design teams.
📝 Abstract
User experience research often uses surveys and interviews, which may miss subconscious user interactions. This study explores eye-tracking and biometric feedback as tools to assess user engagement and cognitive load in digital interfaces. These methods measure gaze behavior and bodily responses, providing an objective complement to qualitative insights. Using empirical evidence, practical applications, and advancements from 2023-2025, we present experimental data, describe our methodology, and place our work within foundational and recent literature. We address challenges like data interpretation, ethical issues, and technological integration. These tools are key for advancing UX design in complex digital environments.