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

TL;DR

This study evaluates psychophysiological measures, ECG and EDA, as objective tools to assess mental workload differences across various human-machine interface designs in automated driving, aiming to develop standardized HMI assessment methods.

Contribution

It demonstrates the effectiveness of ECG and EDA in objectively measuring workload differences between HMI designs in a simulator setting.

Findings

Both measures detected significant workload differences.

ECG and EDA are promising for standardized HMI assessment.

Results support future development of objective evaluation methods.

Abstract

Human-machine Interface (HMI) is critical for safety during automated driving, as it serves as the only media between the automated system and human users. To enable a transparent HMI, we first need to know how to evaluate it. However, most of the assessment methods used for HMI designs are subjective and thus not efficient. To bridge the gap, an objective and standardized HMI assessment method is needed, and the first step is to find an objective method for workload measurement for this context. In this study, two psychophysiological measures, electrocardiography (ECG) and electrodermal activity (EDA), were evaluated for their effectiveness in finding differences in mental workload among different HMI designs in a simulator study. Three HMI designs were developed and used. Results showed that both workload measures were able to identify significant differences in objective mental workload when interacting with in-vehicle HMIs. As a first step toward a standardized assessment method, the results could be used as a firm ground for future studies. Marie Sk{\l}odowska-Curie Actions; Innovative Training Network (ITN); SHAPE-IT; Grant number 860410; Publication date: [29 Sep 2023]; DOI: [10.54941/ahfe1004172]