Recent developments in biometric technology have enabled researchers to study pedestrian stress in real time using tools such as eye tracking, heart rate monitors, and galvanic skin response (GSR) sensors. However, it is not yet clear how closely these sensor-based measurements correspond with individuals’ own reports of stress.
A new study investigated the relationship between sensor-measured and self-reported stress among pedestrians. Thirty participants took part in a semi-naturalistic walking experiment along a 1.2-mile route that included six streets with different built environment features. Each participant wore devices to track heart rate variability (HRV), electrodermal activity (EDA), and gaze behavior through eye-tracking glasses while walking the route. After completing the walk, participants filled out surveys rating their perceived stress levels for each street segment.
Researchers compared 48 different sensor-derived metrics related to stress against the survey ratings using both bivariate and multivariate analyses. The results highlight that analyzing data within subjects and accounting for confounding variables is important when associating biometric data with self-reported outcomes.
The study found that EDA metrics from GSR sensors responded more rapidly to acute episodes of stress, while HRV and gaze-based measures were more reliable indicators of walking-related stress over longer intervals ranging from 30 to 120 seconds.
“We discuss challenges in analyzing and interpreting our sensor measurements and how they measure stress,” according to the research team. “We draw from the theory of risk homeostasis to explain discrepancies between sensor and survey results.”
The researchers suggest that their methodological framework can help guide future efforts on whether—and how—biometric sensors may be used effectively to identify pedestrian stress levels.
