Brainatwork: Logging Cognitive Engagement and Tasks in the Workplace Using Electroencephalography

Today's workplaces are dynamic and complex. Digital data sources such as email and video conferencing aim to support workers but also add to their burden of multitasking. Psychophysiological sensors such as Electroencephalography (EEG) can provide users with cues about their cognitive state. We introduce BrainAtWork, a workplace engagement and task logger which shows users their cognitive state while working on different tasks. In a lab study with eleven participants working on their own real-world tasks, we gathered 16 hours of EEG and PC logs which were labeled into three classes: central, peripheral and meta work. We evaluated the usability of BrainAtWork via questionnaires and interviews. We investigated the correlations between measured cognitive engagement from EEG and subjective responses from experience sampling probes. Using random forests classification, we show the feasibility of automatically labeling work tasks into work classes. We discuss how BrainAtWork can support workers on the long term through encouraging reflection and helping in task scheduling

The sweet smell of success: Enhancing multimedia applications with olfaction

This is the Post-Print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACMOlfaction, or smell, is one of the last challenges which multimedia applications have to conquer. As far as computerized smell is concerned, there are several difficulties to overcome, particularly those associated with the ambient nature of smell. In this article, we present results from an empirical study exploring users' perception of olfaction-enhanced multimedia displays. Findings show that olfaction significantly adds to the user multimedia experience. Moreover, use of olfaction leads to an increased sense of reality and relevance. Our results also show that users are tolerant of the interference and distortion effects caused by olfactory effect in multimedia

Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction

Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-Time and at a granularity that was not possible just a few years ago. Recently, different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper, we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing

Principles for Designing Context-Aware Applications for Physical Activity Promotion

Mobile devices with embedded sensors have become commonplace, carried by billions of people worldwide. Their potential to influence positive health behaviors such as physical activity in people is just starting to be realized. Two critical ingredients, an accurate understanding of human behavior and use of that knowledge for building computational models, underpin all emerging behavior change applications. Early research prototypes suggest that such applications would facilitate people to make difficult decisions to manage their complex behaviors. However, the progress towards building real-world systems that support behavior change has been much slower than expected. The extreme diversity in real-world contextual conditions and user characteristics has prevented the conception of systems that scale and support end-users’ goals. We believe that solutions to the many challenges of designing context-aware systems for behavior change exist in three areas: building behavior models amenable to computational reasoning, designing better tools to improve our understanding of human behavior, and developing new applications that scale existing ways of achieving behavior change. With physical activity as its focus, this thesis addresses some crucial challenges that can move the field forward. Specifically, this thesis provides the notion of sweet spots, a phenomenological account of how people make and execute their physical activity plans. The key contribution of this concept is in its potential to improve the predictability of computational models supporting physical activity planning. To further improve our understanding of the dynamic nature of human behavior, we designed and built Heed, a low-cost, distributed and situated self-reporting device. Heed’s single-purpose and situated nature proved its use as the preferred device for self-reporting in many contexts. We finally present a crowdsourcing system that leverages expert knowledge to write personalized behavior change messages for large-scale context-aware applications.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144089/1/gparuthi_1.pd

OWidgets: A toolkit to enable smell-based experience design

Interactive technologies are transforming the ways in which people experience, interact and share information. Advances in technology have made it possible to generate real and virtual environments with breath-taking graphics and high-fidelity audio. However, without stimulating the other senses such as touch and smell, and even taste in some cases, such experiences feel hollow and fictitious; they lack realism. One of the main stumbling blocks for progress towards creating truly compelling multisensory experiences is the lack of appropriate tools and guidance for designing beyond audio-visual applications. Here we focus particularly on the sense of smell and how smell-based design can be enabled to create novel user experiences. We present a design toolkit for smell (i.e., OWidgets). The toolkit consists of a graphical user interface and the underlying software framework. The framework uses two main components: a Mapper and Scheduler facilitating the device-independent replication of olfactory experiences. We discuss how our toolkit reduces the complexity of designing with smell and enables a creative exploration based on specific design features. We conclude by reflecting on future directions to extend the toolkit and integrate it into the wider audio-visual ecosystem

Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl