Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving
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Summary
This study investigates the detection of discomfort in automated driving using physiological and behavioral parameters, aiming to support the development of real-time adaptive systems for human-machine interaction. As driving automation shifts the human role from active driver to passenger, ensuring comfort is critical for public acceptance and safety, as discomfort can lead to unnecessary or unsafe takeovers. The research, part of the KomfoPilot project, evaluates the utility of commercially available smartbands, pupillometry, and body motion tracking in identifying discomfort during critical driving scenarios. The researchers conducted an empirical study using a fixed-base driving simulator with 40 participants aged 25 to 84. Participants experienced two highly automated driving sessions, each containing three identical, discomfort-inducing scenarios where the ego vehicle approached a slower-moving truck at high speed, resulting in late automated braking and a close minimum distance of 4.2 meters. Perceived discomfort was continuously self-reported via a handset control. Physiological data were collected using a Microsoft Band 2 (heart rate, heart rate variability, skin conductance level), SMI eye-tracking glasses (pupil diameter, blink rate), and a motion tracking system combined with a seat pressure mat (body posture). Data were analyzed by comparing parameter trends during reported discomfort intervals against 10-second pre- and post-intervals, using z-standardization to account for individual baselines. The results indicated that most measured parameters changed significantly during discomfort, with the exception of skin conductance level (SCL), which showed no specific effect. Pupil diameter increased significantly during discomfort intervals, even after correcting for ambient light influences. Eye blink rate decreased, indicating heightened visual attention. Contrary to the hypothesis that stress would increase heart rate, heart rate (HR) decreased significantly during discomfort, while heart rate variability (HRV) diminished as expected. Body motion data confirmed the anticipated behavior, showing a pushback movement away from the approaching truck, reflected in both shoulder motion and increased pressure on the back of the seat. These findings demonstrate that commercially available sensors, particularly smartbands and eye-tracking devices, can reliably detect physiological and behavioral markers of discomfort in automated driving contexts. The study provides a foundational dataset for designing and configuring real-time discomfort detection algorithms. By identifying specific parameter changes associated with subjective discomfort, this research supports the development of adaptive vehicle systems that can adjust driving parameters and information presentation to enhance user comfort and prevent safety-critical takeover situations.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
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- passenger motion sickness comfort
- workload measurement
- drowsiness detection algorithms
- discomfort glare
- simulator sickness
Information type
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- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics, tool software