Moving Beyond the Simulator: Interaction-Based Drunk Driving Detection in a Real Vehicle Using Driver Monitoring Cameras and Real-Time Vehicle DataAlcohol consumption poses a significant public health challenge, presenting serious risks to individual health and contributing to over 700 daily road fatalities worldwide. Digital interventions can play a crucial role in reducing these risks. However, reliable drunk driving detection systems are vital to effectively deliver these interventions. To develop and evaluate such a system, we conducted an interventional study on a test track to collect real vehicle data from 54 participants. Our system reliably identifies non-sober driving with an area under the receiver operating characteristic curve (AUROC) of 0.84 ± 0.11 and driving above the WHO-recommended blood alcohol concentration limit of 0.05 g/dL with an AUROC of 0.80 ± 0.10. Our models rely on well-known physiological drunk driving patterns. To the best of our knowledge, we are the first to (1) rigorously evaluate the potential of (2) driver monitoring cameras and real-time vehicle data for detecting drunk driving in a (3) real vehicle.2025RDRobin Deuber et al.ETH ZürichTeleoperated DrivingHuman Pose & Activity RecognitionCHI
Out of Sight, Out of Mind? Exploring Data Protection Practices for Personal Data in Usable Security & Privacy StudiesAdherence to data protection measures such as pseudonymization or anonymization is critical in human subjects research because it has a direct impact on the confidentiality of participants' sensitive information, trust in research practices, and compliance with ethical and legal standards. Regulations such as the General Data Protection Regulation (GDPR) and guarantees made by researchers in informed consent forms mandate strict protocols for data security. However, compliance with these is not always straightforward. To gain qualitative insights into data protection practices in the field of Usable Security and Privacy (USP), we conducted interviews with 22 practitioners (five professors, eight researchers, nine data protection officers) and one focus group with five researchers. Overall, our results show a high awareness of ethical and legal responsibilities but highlight many practical and procedural issues. Based on these, we make concrete recommendations on how to improve the protection of personal data in research.2025FMFlorin Martius et al.University of BonnAlgorithmic Transparency & AuditabilityPrivacy by Design & User ControlResearch Ethics & Open ScienceCHI
User Attitudes to Content Moderation in Web SearchInternet users highly rely on and trust web search engines, such as Google, to find relevant information online. However, scholars have documented numerous biases and inaccuracies in search outputs. To improve the quality of search results, search engines employ various content moderation practices such as interface elements informing users about potentially dangerous websites and algorithmic mechanisms for downgrading or removing low-quality search results. While the reliance of the public on web search engines and their use of moderation practices is well-established, user attitudes towards these practices have not yet been explored in detail. To address this gap, we first conducted an overview of content moderation practices used by search engines, and then surveyed a representative sample of the US adult population (N=398) to examine the levels of support for different moderation practices applied to potentially misleading and/or potentially offensive content in web search. We also analyzed the relationship between user characteristics and their support for specific moderation practices. We find that the most supported practice is informing users about potentially misleading or offensive content, and the least supported one is the complete removal of search results. More conservative users and users with lower levels of trust in web search results are more likely to be against content moderation in web search.2024AUAleksandra Urman et al.Session 3d: Moderating the Digital SphereCSCW
Self-Efficacy and Security Behavior: Results from a Systematic Review of Research MethodsAmidst growing IT security challenges, psychological underpinnings of security behaviors have received considerable interest, e.g. cybersecurity Self-Efficacy (SE), the belief in one’s own ability to enact cybersecurity-related skills. Due to diverging definitions and proposed mechanisms, research methods in this field vary considerably, potentially impeding replicable evidence and meaningful research synthesis. We report a preregistered systematic literature review investigating (a) cybersecurity SE measures, (b) SE’s proposed roles, and (c) intervention approaches. We minimized selection bias by detailed exclusion criteria, interdisciplinary search strategy, and double coding. Among 174 cybersecurity SE studies (2010-2021) from 18 databases with 55,758 subjects, we identified 173 different SE measures with considerable differences in psychometric quality and validity evidence. We found 276 variables as assumed causes/outcomes of cybersecurity SE and identified 13 intervention designs. This review demonstrates the extent of methodological and conceptual fragmentation in cybersecurity SE research. We offer recommendations to inspire our research community toward standardization.2024NBNele Borgert et al.Ruhr University Bochum, Ruhr University BochumPrivacy Perception & Decision-MakingCybersecurity Training & AwarenessCHI
I see an IC: A Mixed-Methods Approach to Study Human Problem-Solving Processes in Hardware Reverse EngineeringTrust in digital systems depends on secure hardware, often assured through Hardware Reverse Engineering (HRE). This work develops methods for investigating human problem-solving processes in HRE, an underexplored yet critical aspect. Since reverse engineers rely heavily on visual information, eye tracking holds promise for studying their cognitive processes. To gain further insights, we additionally employ verbal thought protocols during and immediately after HRE tasks: Concurrent and Retrospective Think Aloud. We evaluate the combination of eye tracking and Think Aloud with 41 participants in an HRE simulation. Eye tracking accurately identifies fixations on individual circuit elements and highlights critical components. Based on two use cases, we demonstrate that eye tracking and TA can complement each other to improve data quality. Our methodological insights can inform future studies in HRE, a specific setting of human-computer interaction, and in other problem-solving settings involving misleading or missing information.2024RWRené Walendy et al.Ruhr University Bochum, Max Planck Institute for Security and PrivacyEye Tracking & Gaze InteractionVisualization Perception & CognitionCHI
Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk drivingExcessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person’s blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n=30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm.2023KKKevin Koch et al.University of St. GallenHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Human Pose & Activity RecognitionCHI
Taking Mental Health & Well-Being to the Streets: An Exploratory Evaluation of In-Vehicle Interventions in the WildThe increasing number of mental disorders worldwide calls for novel types of prevention measures. Given the number of commuters who spend a substantial amount of time on the road, the car offers an opportune environment. This paper presents the first in-vehicle intervention study affecting mental health and well-being on public roads. We designed and implemented two in-vehicle interventions based on proven psychotherapy interventions. Whereas the first intervention uses mindfulness exercises while driving, the second intervention induces positive emotions through music. Ten ordinary and healthy commuters completed 313 of these interventions on their daily drives over two months. We collected drivers' immediate and post-driving feedback for each intervention and conducted interviews with the drivers after the end of the study. The results show that both interventions have improved drivers' well-being. While the participants rated the music intervention very positively, the reception of the mindfulness intervention was more ambivalent.2021KKKevin Koch et al.University of St. GallenMotion Sickness & Passenger ExperienceMental Health Apps & Online Support CommunitiesCHI