arXiv subjects

Christoph Anderson

Publications and source records attributed to Christoph Anderson.

9 recordsLinked to original sources

arXiv2023

Exploring Emotional and Social Dynamics in Mobile Usage During Home Confinement

Home confinement, a situation experienced by individuals for reasons ranging from medical quarantines, rehabilitation needs, disability accommodations, and remote working, is a common yet impactful aspect of modern life. While essential in various scenarios, confinement within the home environment can profoundly influence mental well-being and digital device usage. Using the COVID-19 lockdown as a case study, this research explores the emotional and social effects of prolonged home confinement on mobile device usage. We conducted an in-situ study with 32 participants, analyzing three weeks of mobile usage data to assess emotional well-being and social dynamics in restricted environments. Our findings reveal that app usage patterns serve as strong indicators of emotional states, offering insights into how digital interactions can reflect and influence well-being during isolation. This study highlights the potential for developing targeted interventions and support systems for individuals in long-term home confinement, including those with chronic illness, recovery needs, or permanent remote work situations.

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arXiv2022

Towards Social Role-Based Interruptibility Management

Pervasive and ubiquitous computing facilitates immediate access to information in the sense of always-on. Information such as news, messages, or reminders can significantly enhance our daily routines but are rendered useless or disturbing when not being aligned with our intrinsic interruptibility preferences. Attention management systems use machine learning to identify short-term opportune moments, so that information delivery leads to fewer interruptions. Humans' intrinsic interruptibility preferences - established for and across social roles and life domains - would complement short-term attention and interruption management approaches. In this article, we present our comprehensive results towards social role-based attention and interruptibility management. Our approach combines on-device sensing and machine learning with theories from social science to form a personalized two-stage classification model. Finally, we discuss the challenges of the current and future AI-driven attention management systems concerning privacy, ethical issues, and future directions.

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arXiv2022

Investigating the Effects of Mood & Usage Behaviour on Notification Response Time

Notifications are one of the most prevailing mechanisms on smartphones and personal computers to convey timely and important information. Despite these benefits, smartphone notifications demand individuals' attention and can cause stress and frustration when delivered at inopportune timings. This paper investigates the effect of individuals' smartphone usage behavior and mood on notification response time. We conduct an in-the-wild study with more than 18 participants for five weeks. Extensive experiment results show that the proposed regression model is able to accurately predict the response time of smartphone notifications using current user's mood and physiological signals. We explored the effect of different features for each participant to choose the most important user-oriented features in order to to achieve a meaningful and personalised notification response prediction. On average, our regression model achieved over all participants an MAE of 0.7764 ms and RMSE of 1.0527 ms. We also investigate how physiological signals (collected from E4 wristbands) are used as an indicator for mood and discuss the individual differences in application usage and categories of smartphone applications on the response time of notifications. Our research sheds light on the future intelligent notification management system.

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arXiv2020

Exploring the Impact of COVID-19 Lockdown on Social Roles and Emotions while Working from Home

In the opening months of 2020, COVID-19 changed the way for which people work, forcing more people to work from home. This research investigates the impact of COVID-19 on five researchers' work and private roles, happiness, and mobile and desktop activity patterns. Desktop and smartphone application usage were gathered before and during COVID-19. Individuals' roles and happiness were captured through experience sampling. Our analysis show that researchers tend to work more during COVID-19 resulting an imbalance of work and private roles. We also found that as working styles and patterns as well as individual behaviour changed, reported valence distribution was less varied in the later weeks of the pandemic when compared to the start. This shows a resilient adaptation to the disruption caused by the pandemic.

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arXiv2019

The Impact of Private and Work-Related Smartphone Usage on Interruptibility

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.

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arXiv2018

Angry or Climbing Stairs? Towards Physiological Emotion Recognition in the Wild

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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arXiv2018

A Survey of Attention Management Systems in Ubiquitous Computing Environments

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.

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arXiv2018

An Ontology-Based Reasoning Framework for Context-Aware Applications

Context-aware applications process context information to support users in their daily tasks and routines. These applications can adapt their functionalities by aggregating context information through machine-learning and data processing algorithms, supporting users with recommendations or services based on their current needs. In the last years, smartphones have been used in the field of context-awareness due to their embedded sensors and various communication interfaces such as Bluetooth, WiFi, NFC or cellular. However, building context-aware applications for smartphones can be a challenging and time-consuming task. In this paper, we describe an ontology-based reasoning framework to create context-aware applications. The framework is based on an ontology as well as micro-services to aggregate, process and represent context information.

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arXiv2018

Assessment of Social Roles for Interruption Management: A New Concept in the Field of Interruptibility

Determining and identifying opportune moments for interruptions is a challenging task in Ubiquitous Computing and Human-Computer-Interaction. The current state-of-the-art approaches do this by identifying breakpoints either in user tasks, activities or by processing social relationships and contents of interruptions. However, from a psychological perspective, not all of these breakpoints represent opportune moments for interruptions. In this paper, we propose a new concept in the field of interruptibility. The concept is based on role theory and psychological interruption research. In particular, we argue that social roles which define sets of norms, expectations, rules and behaviours can provide useful information about the user's current context that can be used to enhance interruption management systems. Based on this concept, we propose a prototype system architecture that uses social roles to detect opportune moments for interruptions.

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