This paper contributes to our understanding of user-centered attacks on smartphones. In particular, we investigate the likelihood of so-called shoulder surfing attacks during touch-based unlock events and provide insights into users' views and perceptions. To do so, we ran a two-week in-the-wild study (N=12) in which we recorded images with a 180-degree field of view lens that was mounted on the smartphone's front-facing camera. In addition, we collected contextual information and allowed participants to assess the situation. We found that only a small fraction of shoulder surfing incidents that occur during authentication are actually perceived as threatening. Furthermore, our findings suggest that our notions of (un)safe places need to be rethought. Our work is complemented by a discussion of implications for future user-centered attack-aware systems. This work can serve as a basis for usable security researchers to better design systems against user-centered attacks.

Workplace stress is a growing problem, which is often not identified by sufferers and those around them until it becomes chronic. Moreover, admitting to feel stressed is a highly private and therefore sensitive topic, particularly at work. Informed by the findings from six in-depth interviews, we designed the manually self-adjusted wearable BuSiNec: a Busyness Signifying Necklace. 18 participants wore it at their workplace and reported on their experiences and their usage behavior through a diary and in a focus group interview. Our findings indicate that BuSiNec supports self-reflection on stress and further stimulates valuable discussions among co-users which in turn increased mutual consideration. Participants were conscious of their their displayed busyness status towards others and used the display to encourage or discourage discussions and interruptions, or as a warning of upcoming hurry. From these findings, we derive recommendations for the integration of affective state information in wearable displays.

Since the release of the first activity tracker, there has been a steady increase in the number of sensors embedded in wearable devices and with it in the amount and diversity of information that can be derived from these sensors. This development leads to novel privacy threats for users. In a web survey with 248 participants, we explored whether users' willingness to share private data is dependent on how the data is requested by an application. Specifically, requests can be formulated as access to sensor data or as access to information derived from the sensor data (e.g., accelerometer vs. sleep quality). We show that non-expert users lack an understanding of how the two representation levels relate to each other. The results suggest that the willingness to share sensor data over derived information is governed by whether the derived information has positive or negative connotations (e.g., training intensity vs. life expectancy). Using the results of the survey, we derive implications for supporting users in protecting their private data collected via wearable sensors.

Understanding social perception is important for designing mobile devices that are socially acceptable. Previous work not only investigated the social acceptability of mobile devices and interaction techniques but also provided tools to measure social acceptance. However, we lack a robust model that explains the underlying factors that make devices socially acceptable. In this paper, we consider mobile devices as social objects and investigate if the stereotype content model (SCM) can be applied to those devices. Through a study that assesses combinations of mobile devices and group stereotypes, we show that mobile devices have a systematic effect on the stereotypes' warmth and competence. Supported by a second study, which combined mobile devices without a specific stereotypical user, our result suggests that mobile devices are perceived stereotypically by themselves. Our combined results highlight mobile devices as social objects and the importance of considering stereotypes when assessing social acceptance of mobile devices.

The Stroop Test is a well known and regularly employed stressor in laboratory research. In contrast to other methods, it is not based on fear of physical harm or social shame. Consequently, it is more likely accepted by a wide population. In our always-on, technology-driven, social-media centered world, large-scale in-field stress research will need adequate experimental tools to explore the increasing prevalence of stress-related diseases without bringing subjects into laboratories. This is why we designed the Stroop Room: A virtual reality-based adaptation of the Stroop Test using elements of the virtual world to extend the demands of the original test and at the same time make it easily accessible. It is open source and can be used and improved by anyone as an in-the-wild, repeatable, laboratory-quality stressor. In this work, the method is presented and an evaluation study described, to demonstrate its effectiveness in provoking cognitive stress.

Fitness trackers not just provide easy means to acquire physiological data in real-world environments due to affordable sensing technologies, they further offer opportunities for physiology-aware applications and studies in HCI; however, their performance is not well understood. In this paper, we report findings on the quality of 3 sensing technologies: PPG-based wrist trackers (Apple Watch, Microsoft Band 2), an ECG-belt (Polar H7) and reference device with stick-on ECG electrodes (Nexus 10). We collected physiological (heart rate, electrodermal activity, skin temperature) and subjective data from 21 participants performing combinations of physical activity and stressful tasks. Our empirical research indicates that wrist devices provide a good sensing performance in stationary settings. However, they lack accuracy when participants are mobile or if tasks require physical activity. Based on our findings, we suggest a textitDesign Space for Wearables in Research Settings and reflected on the appropriateness of the investigated technologies in research contexts.

The connection between stress and smartphone usage behavior has been investigated extensively. While the prediction results using machine learning are encouraging, the challenge of how to cope with data loss remains. Addressing this problem, we propose an Intelligent Recommender System for logging stress based on adding a subjective user data-based validation to predictions made by intelligent algorithms. In a user study involving 731 daily stress self-reports from 30 participants we found discrepancies between subjective and smartphone usage data, i.e. battery, call information, or network usage. Despite the good prediction accuracy of 65% using a Random Forest classifier, combining both information would be beneficial for avoiding data and improving prediction accuracy. For realizing such a system (i.e., a mobile application), we propose three design recommendations, based on the capabilities of frequently used machine learning classifiers, enabling users to annotate their daily stress levels with a predict-and-validate methodology.

Smartphones, wearables, and other mobile devices often use tactile feedback for notifying users. This feedback type proved to be beneficial since it does not occupy the visual or auditory channel. However, it still can be distracting in other situations such as when users are already stressed. To investigate tactile feedback patterns which do not increase the user's stress level, we developed two wrist-worn prototypes capable of providing tactile feedback (i.e., vibrotactile and pressure-based feedback). Further, we conducted a user-study with 14 participants comparing both feedback types. The results suggest that vibrotactile feedback increases the user's stress level more, compared to pressure-based feedback particularly applied when the user currently has a low stress level. Consequently, we present implications for designing notifications for mobile and wearable devices.

More and more industrial manufacturing companies are outsourcing assembly tasks to sheltered work organizations where cognitively impaired workers are employed. To facilitate these assembly tasks assistive systems have been introduced to provide cognitive assistance. While previous work found that these assistive systems have a great impact on the workers' performance in giving assembly instructions, these systems are further capable of detecting errors and notifying the worker of an assembly error. However, the topic of how assembly errors are presented to cognitively impaired workers has not been analyzed scientifically. In this paper, we close this gap by comparing tactile, auditory, and visual error feedback in a user study with 16 cognitively impaired workers. The results reveal that visual error feedback leads to a significantly faster assembly time compared to tactile error feedback. Further, we discuss design implications for providing error feedback for workers with cognitive impairments.