This paper is useful because it tests a problem that is very close to the one I am now facing: what happens to wearable physiological data when the person is moving through a real-world environment?
The study is about driving rather than autism or airports, but the methodological problem transfers well. The researchers ask whether wearable signals can distinguish different driving contexts, and they are careful to separate a signal reflecting the driver's physiological response to context from the road environment itself.
That distinction matters for my project. If a wearable changes during a busy airport journey, the signal is not a direct measurement of the airport and it is not a direct measurement of sensory overload. It is a bodily response occurring within a particular context.
Em and Teoh analysed wearable data collected during real-world driving using an Empatica EmbracePlus.
The dataset included:
The wearable recorded physiological, motion and temperature-related information. The paper then tested whether those signals could classify the driving context.
This is important because the study is not asking whether the wearable can diagnose stress or drowsiness. It is asking something more basic: does context leave a detectable signature in wearable data?
One of the strongest points in the paper is the authors' warning that a wearable signal should not be confused with the environment itself.
A physiological or motion signal reflects the driver's response to the context, not the road environment directly.
For my airport project, the same principle applies.
A change in EDA or heart rate while somebody is moving through security might be associated with the situation, but the signal does not tell me: