"Focus Flow" An approach to Distraction Free Environment

2015

A large amount of software products offer a wide<br> range and number of features. This is called featuritis or creeping<br> featurism and tends to rise with each release of the product. Feautiris<br> often adds unnecessary complexity to software, leading to longer<br> learning curves and overall confusing the users and degrading their<br> experience. We take a look to a new design approach tendency that<br> has been coming up, the so-called "What You Get is What You<br> Need" concept that argues that products should be very focused,<br> simple and with minimalistic interfaces in order to help users conduct<br> their tasks in distraction-free ambiences. This isn't as simple to<br> implement as it might sound and the developers need to cut down<br> features. Our contribution illustrates and evaluates this design method<br> through a novel distraction-free diagramming tool named Delineato<br> P...

2007

Multitasking environments cause people to be interrupted constantly, often disrupting their ongoing activities and impeding reaching their goals. This thesis presents a disruption reducing approach designed to support the user's goals and optimize productivity that is based on a model of the user's receptivity to an interruption. The model uses knowledge of the interruption content, context and priority of the task(s) in progress, user actions and goal-related concepts to mediate interruptions. The disruption management model is distinct from previous work by the addition of implicit sensors that deduce the interruption content and user context to help determine when an interruption will disrupt an ongoing activity. Domain-independent implicit sensors include mouse and keyboard behaviors, and goal-related concepts extracted from the user documents. The model also identifies the contextual relationship between interruptions and user goals as an important factor in how interruptions are controlled. The degree to which interruptions are related to the user goal determines how those interruptions will be received. We tested and evolved the model in various cases and showed significant improvement in both productivity and satisfaction. A disruption manager application controls interruptions on common desktop computing activities, such as web browsing and instant messaging. The disruption manager demonstrates that mediating interruptions by supporting the user goals can improve performance and overall productivity. Our evaluation shows an improvement in success of over 25% across prioritization conditions for real life computing environments. Goal priority and interruption relevance play an important role in the interruption decision process and several experiments these factors on people's reactions and availability to interruptions, and overall performance. These experiments demonstrate that people recognize the potential benefits of being interrupted and adjust their susceptibility to interruptions during highly prioritized tasks. The outcome of this research includes a usable model that can be extended to tasks as diverse as driving an automobile and performing computer tasks. This thesis supports mediating technologies that will recognize the value of communication and control interruptions so that people are able to maintain concentration amidst their increasingly busy lifestyles.

Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services

Mobile devices are being used in more and more complex settings such as cars or medical environments and these environments are causing serious distractions for the mobile user. This paper presents novel research that investigates mobile user experiences when interacting with cognitively demanding distractions. This research finds that, surprisingly, the user's primary task is not always affected by the distraction but, in this case, the actual interaction between user and device is. This observation initially appears to contradict current research which suggests that a distraction will affect the primary task. The main conclusion of this paper is that a user, when dealing with distraction, can balance their cognitive processes by applying less cognitive resources to the mobile device interaction in order to maintain their performance at the primary task. Essentially, the interface can appear more difficult and less user friendly.

Applications helping us to maintain the focus on work are called " Zenware " (from concentration and Zen). While form factors, use cases and functionality vary, all these applications have a common goal: creating uninter‐ rupted, focused attention on the task at hand. The rise of such tools exemplifies the users' desire to control their attention within the context of omnipresent distraction. In expert interviews we investigate approaches in the context of attention-management at the workplace of knowledge workers. To gain a broad understanding, we use judgement sampling in interviews with experts from several disciplines. We especially explore how focus and flow can be stimulated. Our contribution has four components: a brief overview on the state of the art (1), a presentation of the results (2), strategies for coping with digital distractions and design guidelines for future Zenware (3) and an outlook on the overall potential in digital work environments (4).

2005

This paper reports on TaskTracer -a software system being designed to help highly multitasking knowledge workers rapidly locate, discover, and reuse past processes they used to successfully complete tasks. The system monitors users' interaction with a computer, collects detailed records of users' activities and resources accessed, associates (automatically or with users' assistance) each interaction event with a particular task, enables users to access records of past activities and quickly restore task contexts. We present a novel Publisher-Subscriber architecture for collecting and processing users' activity data, describe several different user interfaces tried with TaskTracer, and discuss the possibility of applying machine learning techniques to recognize/predict users' tasks.

Procedia Manufacturing, 2015

Multitasking is common in our everyday life. Often people multitask either consciously or otherwise, whether talking on the phone while driving or visiting social networking sites while completing a work assignment. Students are commonly engaged in multiple tasks while completing homework. Research shows that multitasking negatively impacts one's performance. System designers can examine various ways to minimize negative effects of multitasking. Systems can be designed to automatically set up reminder alerts reminding users to attend their primary task. In order to examine whether reminders would improve user performance when multitasking, we set up a web-based laboratory experiment with a multi-tabbed user interface. The first tab contained slides with study material for a world history quiz. The remaining tabs each contained different games. Participants had 20-minutes to study and were able to switch tasks whenever they desired. Participants were randomized into one of two conditions: reminders or no-reminders. Participants in the reminder condition received reminder alerts every one minute off-task reminding them of the primary task. As long as the user was on the slides tab, no reminders appeared. Participants in the control condition did not receive any reminders. Data for 66 participants was analyzed. The results of the study suggest that receiving reminders about a task when multitasking may be helpful for females. However, for males, these reminder alerts were not helpful, and in fact appear to increase the negative effects of multitasking by causing more switches and possibly lower performance.

Employee attention is critical for successful task completion, learning, and productivity. However, modern distractions such as notifications, applications, and online information regularly disrupt focus and reduce work effectiveness. Research aims to improve employee attention and task efficiency by creating an advanced digital distraction blocking system that detects distracted behaviors and intervenes to maintain focus. Video recordings of employees executing tasks in online and digital settings were gathered to capture natural attention and distraction patterns. Frames were normalized, faces were identified, and irrelevant background noise was eliminated to ensure consistent data quality. Frame blocking is utilized for preprocessing, and the VGG16-CNN was used to extract deep visual features, including facial expressions, gaze direction, head orientation, and micromovements that indicate attentiveness or distraction. A Decision Tree Tuned Scalable Spiking Network (DT-S2Net), which combines Decision Trees (DT) for interpretable classification with Scalable Spiking Neural Networks (S2NN) for temporal pattern detection of attention and distraction behaviors. The DT-S2Net method outperformed the existing methods, achieving higher accuracy (99.01%), precision (99.57%), recall (98.77%) and F1-score (99.0%) in recognizing distracted states, allowing for timely digital interventions such as limiting notifications or irrelevant programs, resulting in faster task completion and sustained focus.

… of the SIGCHI conference on Human …, 2004

We report on a diary study of the activities of information workers aimed at characterizing how people interleave multiple tasks amidst interruptions. The week-long study revealed the type and complexity of activities performed, the nature of the interruptions experienced, and the difficulty of shifting among numerous tasks. We present key findings from the diary study and discuss implications of the findings. Finally, we describe promising directions in the design of software tools for task management, motivated by the findings.

Zenodo (CERN European Organization for Nuclear Research), 2023

The rise of remote work has necessitated the development of innovative solutions that bridge the gap between physical and virtual workspaces. RealTES (Real Time Environmental simulation) is a virtual office application that seeks to create a simulated in-office working environment by providing customizable workspaces, private meeting rooms, and deep-focus spaces to enhance productivity. The application is designed to help users maintain work-life balance by providing tools to enhance productivity without compromising on one's health and well-being. RealTES uses a two-dimensional workspace built with the aid of a Game Engine called Unity , real-time audio video communication platform Agora.io [6], and it aims to be browser-first, so that users do not need to download additional software. Users can navigate the virtual office using keyboard or mouse inputs and can connect seamlessly with other users in the same room via audio and video channels using the WebRTC technology. A secondary application system in RealTES uses computer vision to determine a user's alertness levels and workload by analyzing facial cues and advises the user to take breaks accordingly. The application also includes integrated reminders, alerts, and timers to help users manage their deadlines. RealTES seeks to improve digital interactions by incorporating a social aspect into the communication process, creating a sense of presence within an organization. However, it is important to note that there are some limitations to this approach, such as the difficulty in conveying physical sensations beyond audio and visual stimulation and the potential for network faults that can disrupt the online application.

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Due to the high number and cost of interruptions at work, several approaches have been suggested to reduce this cost for knowledge workers. These approaches predominantly focus either on a manual and physical indicator, such as headphones or a closed office door, or on the automatic measure of a worker's interruptibilty in combination with a computer-based indicator. Little is known about the combination of a physical indicator with an automatic interruptibility measure and its long-term impact in the workplace. In our research, we developed the FlowLight, that combines a physical traffic-light like LED with an automatic interruptibility measure based on computer interaction data. In a large-scale and long-term field study with 449 participants from 12 countries, we found, amongst other results, that the FlowLight reduced the interruptions of participants by 46%, increased their awareness on the potential disruptiveness of interruptions and most participants never stopped using it.