This paper caught my attention because it is very close to the system architecture I am now considering. Rather than relying on physiology alone, Heng and Yavari combine environmental conditions, movement/context and physiological data in an attempt to model personal comfort in real-world conditions.
That is directly relevant to my emerging airport proposition. I am currently asking whether physiological change becomes more useful when it is interpreted alongside what the traveller is doing and what is happening around them, rather than treating a heart-rate or electrodermal change as meaningful on its own.
The paper is useful partly because its results are not especially strong. It shows that building a multimodal system is technically possible, but it also shows how difficult it is to infer a person's state reliably once sensing moves out of the laboratory.
Heng and Yavari developed a Comfort Framework that combines three layers of information:
The framework was intended to cover thermal, acoustic, visual and air-quality conditions. However, the pilot only provided meaningful empirical validation for thermal and acoustic signals.
The study was also very small: it was a single-subject pilot involving 18 free-living sessions in Melbourne. The system produced 418 labelled 60-second windows, with 10 sessions contributing valid leave-one-session-out evaluation folds.
This means I need to treat the work as a proof of pipeline and methodological exploration, not evidence that the model generalises to other people.
One of the most useful parts of this paper is that the authors deliberately moved away from a controlled chamber.
In a real environment, physiology is being affected by many things at once. Walking, posture, temperature, noise, movement, location and ordinary activity can all alter the signals a wearable records.
The authors therefore included kinematic context rather than pretending the physiological signal could be interpreted in isolation.
This is particularly relevant to an airport. A traveller may be walking quickly, carrying luggage, queuing, sitting, rushing after a gate change or standing still at security. Any of those activities could affect heart rate or HRV without meaning that the person needs sensory or accessibility support.
For my project, this strengthens the idea that movement data is not just another sensor channel. It may be necessary for interpreting whether a physiological change is even unusual in context.
The strongest Random Forest model achieved an F1 macro score of 0.456 ± 0.151 across the valid evaluation folds.