Multi-point STM offers a great range of parameters (i.e., drawing frequency, number of points) to produce different tactile sensations. However, existing studies offer limited insight on the effects of these parameters, and ignore their effect on the physical stimuli delivered, limiting effective haptic design. We propose a two-stage model to predict response to multi-point STM. The first stage predicts physical stimulus properties with 7.8% error, while the second stage predicts mean and spread of perceived intensity with 8.0 % and 8.8% error. We report 3 studies conducted to derive this model: one to characterize physical stimuli, another one measuring user perceptual thresholds, and a third one measuring user’s perceptual response to multi-point STM. Besides, we characterize 4 effects that influence device performance, confirm if previous effects reported are due to physical or perceptual effects (or both) and derive recommendations for manufacturers, haptic designers and HCI researchers.

Comfort is a subjective experience that people attend to in everyday life including in cars where they are constrained in movement. Could intelligent cars sense their comfort levels for the purpose of maximizing it? To address this, first, we present a new dataset (available on request) of physical measures (skin temperature, blood volume pulse, electrodermal activity, and motion capture) and subjective thermal, sitting, and mental relaxation experience variables captured in semi-ecological settings in a car. Second, we provide an in-depth analysis of the relationship between passengers’ thermal experiences and physiological responses in the collected data. Our findings highlight complex duality in the relationship of thermal experience with heart rate variability and skin temperature variability. We discuss the practical implications that this may have for designing machine learning architectures for automatic detection of thermal discomfort.