State recognition in well-known and customizable environments such as vehicles enables novel insights into users and potentially their intentions. Besides safety-relevant insights into, for example, fatigue, user experience-related assessments become increasingly relevant. An importance ranking of such signals indicates higher impact on prediction for signals like ambient temperature, ambient humidity, radiation temperature, and skin temperature.
Leveraging modern machine learning architectures enables us to not only automatically recognize human thermal comfort state but also predict future states.
We provide details on how we train a recurrent network-based classifier and, thus, perform an initial performance benchmark of our proposed thermal comfort dataset.
Ultimately, we compare our collected dataset to publicly available datasets. This Figure shows the two stages of our dataset introduction. On the left side, a person is depicted with two fans blowing warm or cold air. This leads to a thermal comfort rating, indicated via a scale from warm to cold.
The figure then shows the various measurements, such as ambient temperature. On the right side, the figure shows via a clock that 10 sec time steps were used to predict the thermal comfort. To improve the user experience, there is a need for accurate and reliable recognition, interpretation, and understanding of current passenger states, as this ensures the execution of adjustments based on user preferences stampftowards.
