Workload Assessment of Human-Machine Interface: A Simulator Study with Psychophysiological Measures

Liu, Yuan-cheng; Figalová, Nikol; Jürgen, Jürgen; Hock, Philipp; Baumann, Martin; Bengler, Klaus · 2023 · Crossref

DOI: 10.54941/ahfe1004172

archive: archived pipeline: cataloged verified

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Summary

This study addresses the need for objective, standardized methods to evaluate Human-Machine Interfaces (HMI) in automated driving systems. Current HMI assessments rely heavily on subjective questionnaires, which are prone to bias and lack real-time capability. To support the development of a Transparency Assessment Method (TRASS), the authors investigated whether psychophysiological measures could effectively detect differences in mental workload across distinct HMI designs. The research specifically evaluated electrocardiography (ECG) and electrodermal activity (EDA) as objective indicators of cognitive load during interactions with SAE Level 2 automated driving systems. The experimental design involved 24 licensed participants (mean age 27.92) engaging in a static driving simulator study. Three HMI designs were tested: "Fog" (low contrast, small icons, no feedback), "Trans" (high contrast, large icons, clear feedback), and "Trans-fog" (high contrast visuals but no feedback). The "Fog" design was hypothesized to induce the highest workload, while "Trans" was expected to yield the lowest. Psychophysiological data were collected using ECG electrodes in a Lead II configuration and EDA electrodes mounted on the left foot to minimize motion artifacts. Data were sampled at 500 Hz and preprocessed in MATLAB. The primary metrics analyzed were the root mean square of successive differences between normal heartbeats (RMSSD) from ECG and skin conductance response (SCR) from EDA. These objective measures were compared against subjective assessments using the NASA-Task Load Index (NASA-TLX) and a custom transparency questionnaire. The results demonstrated that both psychophysiological measures successfully identified significant differences in mental workload among the three HMI designs. Repeated measures ANOVA revealed significant effects for both RMSSD ($F(2,522) = 7.25, p < 0.001$) and SCR ($F(2,522) = 3.372, p = 0.035$). Post-hoc comparisons indicated that the "Trans" HMI produced the highest RMSSD values, indicating the lowest cognitive load, and significantly lower SCR values compared to the "Fog" HMI. No significant differences were found between the "Fog" and "Trans-fog" designs for either metric. These objective findings aligned with subjective data, where the "Trans" HMI received the lowest NASA-TLX scores and highest transparency ratings. The study concludes that ECG and EDA are effective, objective tools for assessing mental workload in HMI evaluation. By validating these psychophysiological measures, the research provides a foundation for integrating continuous, real-time workload monitoring into the TRASS framework. This advancement supports the development of standardized, efficient HMI assessment protocols, moving beyond subjective heuristics to enable more reliable design and evaluation processes for automated vehicle interfaces.

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Topics

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• workload measurement
• mental demand
• automation
• neuro workload indices
• stress driving

Information type

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• Empirical Findings: physiological data, self report data
• Methodological Resource: validation psychometrics