Notifications are a core feature of mobile phones. They inform users about a variety of events. Users may take immediate action or ignore them depending on the importance of a notification as well as their current context. The nature of notifications is manifold, applications use them both sparsely and frequently. In this paper we present the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions. We derive a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users. Using a data-driven approach, we break down what users like and dislike about notifications. Our results reveal differences in importance of notifications and how users value notifications from messaging apps as well as notifications that include information about people and events. Based on these results we derive a number of findings about the nature of notifications and guidelines to effectively use them.
Spearcons have broadened the taxonomy of nonspeech auditory cues. Users can benefit from the application of spearcons in real devices.
Boredom is a common human emotion which may lead to an active search for stimulation. People often turn to their mobile phones to seek that stimulation. In this paper, we tackle the challenge of automatically inferring boredom from mobile phone usage. In a two-week in-the-wild study, we collected over 40,000,000 usage logs and 4398 boredom self-reports of 54 mobile phone users. We show that a user-independent machine-learning model of boredom -leveraging features related to recency of communication, usage intensity, time of day, and demographics-can infer boredom with an accuracy (AUCROC) of up to 82.9%. Results from a second field study with 16 participants suggest that people are more likely to engage with recommended content when they are bored, as inferred by our boredom-detection model. These findings enable boredom-triggered proactive recommender systems that attune their users' level of attention and need for stimulation.
Current digital systems are largely blind to users' cognitive states. Systems that adapt to users' states show great potential for augmenting cognition and for creating novel user experiences. However, most approaches for sensing cognitive states, and cognitive load specifically, involve obtrusive technologies, such as physiological sensors attached to users' bodies. This paper present an unobtrusive indicator of the users' cognitive load based on thermal imaging that is applicable in real-world. We use a commercial thermal camera to monitor a person's forehead and nose temperature changes to estimate their cognitive load. To assess the effect of different levels of cognitive load on facial temperature we conducted a user study with 12 participants. The study showed that different levels of the Stroop test and the complexity of reading texts affect facial temperature patterns, thereby giving a measure of cognitive load. To validate the feasibility for real-time assessments of cognitive load, we conducted a second study with 24 participants, we analyzed the temporal latency of temperature changes. Our system detected temperature changes with an average latency of 0.7 seconds after users were exposed to a stimulus, outperforming latency in related work that used other thermal imaging techniques. We provide empirical evidence showing how to unobtrusively detect changes in cognitive load in real-time. Our exploration of exposing users to different content types gives rise to thermal-based activity tracking, which facilitates new applications in the field of cognition-aware computing. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Cognitive science; • Hardware → Displays and imagers;
In this paper, we demonstrate the existence of a bidirectional causal relationship between smartphone application use and user emotions. In a two-week long in-the-wild study with 30 participants we captured 502,851 instances of smartphone application use in tandem with corresponding emotional data from facial expressions. Our analysis shows that while in most cases application use drives user emotions, multiple application categories exist for which the causal effect is in the opposite direction. Our findings shed light on the relationship between smartphone use and emotional states. We furthermore discuss the opportunities for research and practice that arise from our findings and their potential to support emotional well-being.
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