Assessing Objective Indicators of Users' Cognitive Load During Proactive In-Car Dialogs

Abstract

Using Personal Assistants (PAs) via voice becomes increasingly usual as more and more devices in different environments offer this capability, such as Google Assistant, Amazon Alexa, Apple Siri, Microsoft Cortana, Mercedes-Benz MBUX or BMW Intelligent Personal Assistant. PAs help users to set reminders, find their way through traffic, or send messages to friends and colleagues. While serving the users' needs, PAs constantly collect personal data in order to personalize their services and adapt their behavior. In order to find out which objective Cognitive Load (CL) indicators reflect the users' perception of proactive system behavior in six specific use cases of an in-car PA, we conducted a Wizard of Oz study in a driving simulator with 42 participants. We varied traffic density and tracked physiological data, such as heart rate (HR) and electrodermal activity (EDA). We assessed the users' CL during the interaction with the PA by employing these data as well as real-time driving data (RTDA) via the Controller Area Network (CAN bus). The results show that physiological data like HR and EDA are not suitable to be used as indicators for the users' CL in this experiment. This is because the tracked physiological data do not show significant differences with respect to different traffic densities or proactivity. At the same time it has to be discussed whether the used type of recording physiological data is robust enough for our purposes. Concerning driving data, only the acceleration parameter showed a tendency towards differences between age groups, though insignificantly. The same is valid for the steering angle parameter when comparing male and female users. For future work, we plan to additionally evaluate subjective CL measures and other ratings to see whether these show more significant differences between the (non-)proactive assistants, traffic densities, or use cases.