Abstract. As cars become increasingly computerized, automatic emotion detection and affective computing provides a promising basis for future-oriented human-computer interaction (HCI) in cars. However, we are still facing severe problems when trying to detect the users' emotional state reliably. This experimental study investigated grip-strength as a new non-invasive method to detect emotions directly in an automobile context. A positive emotion (happiness) and a negative emotion (anger) were examined regarding their influence on gripstrength applied to the steering wheel. Results confirmed and extended preliminary findings: Drivers' grip-strength slightly increased while driving a car when happiness was experienced and especially decreased when anger was experienced. Implications for further research as well as for praxis are outlined.

Emotion detection provides a promising basis for designing futureoriented human centered design of Human-Machine Interfaces. Affective Computing can facilitate human-machine communication. Such adaptive advanced driver assistance systems (ADAS) which are dependent on the emotional state of the driver can be applied in cars. In contrast to the majority of former studies that only used static recognition methods, we investigated a new dynamic approach for detecting emotions in facial expressions in an artificial setting and in a driving context. By analyzing the changes of an area defined by a number of dots that were arranged on participants' faces, variables were extracted to classify the participants' emotions according to the Facial Action Coding System. The results of our novel way to categorize emotions lead to a discussion on additional applications and limitations that frames an attempted approach of emotion detection in cars. Implications for further research and applications are outlined.

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