Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.:2 • C. Anderson et al. Key takeaway points⋆ Attention is captured and steered by external and internal stimuli, while stimulus properties, such as its duration, location, intensity, etc., impact the user's reaction to the stimulus. Multimodal alert types, e.g., sound, light, vibration, and multiple device environments call for a coordinated judicious use of alerting in ubiquitous computing. ⋆ Limited cognitive capacities and threaded task processing imply that, in order to minimize disruptions, interruptions need to arrive at task boundaries or during routine tasks, should allow for task state rehearsal, and support context retrieval through hints presented to the user after an interruption. ⋆ Sensor data from ubiquitous computing devices reveals a user's location, physical activity, collocation with other people, and other information of a user's context that can be related to interruptibility. Next generation wearable devices and personalized machine learning models promise to bring us closer to direct inference of a user's cognitive processes. ATTENTION AND INTERRUPTION: DEFINITIONS AND STRATEGIESIn this section, we review definitions of the terms attention and interruption. We examine the connection between attention shifting and interruptions and discuss how interruptions can be handled through attention management systems in ubiquitous environments. What is Attention?There is no common understanding of attention in the literature. Attention is often considered as selective processing of incoming sensory information [33], with limited capacity [23] and reactive and deliberate processes [92]. Attention is also referred to as the ability to ignore irrelevant information [24]. The process of selecting stimuli can be voluntary or be steered by external events. The former ty...
In the last decade, the effects of interruptions through mobile notifications have been extensively researched in the field of Human-Computer Interaction. Breakpoints in tasks and activities, cognitive load, and personality traits have all been shown to correlate with individuals' interruptibility. However, concepts that explain interruptibility in a broader sense are needed to provide a holistic understanding of its characteristics. In this paper, we build upon the theory of social roles to conceptualize and investigate the correlation between individuals' private and work-related smartphone usage and their interruptibility. Through our preliminary study with four participants over 11 weeks, we found that application sequences on smartphones correlate with individuals' private and work roles. We observed that participants engaged in these roles tend to follow specific interruptibility strategies -integrating, combining, or segmenting private and work-related engagements. Understanding these strategies breaks new ground for attention and interruption management systems in ubiquitous computing.
Inferring emotions from physiological signals has gained much traction in the last years. Physiological responses to emotions, however, are commonly interfered and overlapped by physical activities, posing a challenge towards emotion recognition in the wild. In this paper, we address this challenge by investigating new features and machine-learning models for emotion recognition, non-sensitive to physical-based interferences. We recorded physiological signals from 18 participants that were exposed to emotions before and while performing physical activities to assess the performance of non-sensitive emotion recognition models. We trained models with the least exhaustive physical activity (sitting) and tested with the remaining, more exhausting activities. For three different emotion categories, we achieve classification accuracies ranging from 47.88% -73.35% for selected feature sets and per participant. Furthermore, we investigate the performance across all participants and of each activity individually. In this regard, we achieve similar results, between 55.17% and 67.41%, indicating the viability of emotion recognition models not being influenced by single physical activities.
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