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  • 1.
    Moradi, Fatemeh
    et al.
    Umeå University, Faculty of Social Sciences, Department of Informatics.
    Wiberg, Mikael
    Umeå University, Faculty of Social Sciences, Department of Informatics.
    NEAT-Lamp and Talking Tree: Beyond Personal Informatics towards Active Workplaces2018In: Computers, E-ISSN 2073-431X, Vol. 7, no 4Article in journal (Refereed)
    Abstract [en]

    A growing number of personal informatics (PI) systems have been designed to break the habit of prolonged sitting and to encourage physical activity during workdays and leisure hours. Few studies, however, have investigated the nature of local movement and mobility in workspaces. Relatively little is known about how such movement patterns are shaped and in what ways micro-mobility in workplaces could be increased. By undertaking a concept-driven design approach, and on the basis of our ethnographic prestudy, we introduce a conceptual framework. In this conceptual framework, we indicate the five main agencies that shape local movement and mobility among office workers. On the basis of this empirical and conceptual work, two prototypes, the non-exercise activity thermogenesis (NEAT)-Lamp and Talking Tree, have been designed, implemented and observed in an office environment. This paper describes this design project and articulates the role of discussions in socially established settings in work environments in order to increase daily movement. The paper concludes by highlighting not only technology, but also collective reflections to spark behavioral change in office environments as social settings. 

  • 2.
    Pordel, Mostafa
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Australian National University.
    Hellström, Thomas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Semi-Automatic Image Labelling Using Depth Information2015In: Computers, ISSN 2073-431X, E-ISSN 2073-431X, Vol. 4, no 2, p. 142-154Article in journal (Refereed)
    Abstract [en]

    Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a need for RGB-depth adapted tools. We present a new interactive labeling tool that partially automates image labeling, with two major contributions. First, the method extends the concept of image segmentation from RGB to RGB-depth using Fuzzy C-Means clustering, connected component labeling and superpixels, and generates bounding pixels to extract the desired objects. Second, it minimizes the interaction time needed for object extraction by doing an efficient segmentation in RGB-depth space. Very few clicks are needed for the entire procedure compared to existing, tools. When the desired object is the closest object to the camera, which is often the case in robotics applications, no clicks at all are required to accurately extract the object.

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