13 Zentrale Universitätseinrichtungen

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/14

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Institute: search.filters.UBSInstitut.Visualisierungsinstitut der Universität StuttgartSubject: search.filters.subject.004Author: search.filters.author.Heyen, Frank

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    Touching data with PropellerHand
    (2022) Achberger, Alexander; Heyen, Frank; Vidackovic, Kresimir; Sedlmair, Michael
    Immersive analytics often takes place in virtual environments which promise the users immersion. To fulfill this promise, sensory feedback, such as haptics, is an important component, which is however not well supported yet. Existing haptic devices are often expensive, stationary, or occupy the user’s hand, preventing them from grasping objects or using a controller. We propose PropellerHand, an ungrounded hand-mounted haptic device with two rotatable propellers, that allows exerting forces on the hand without obstructing hand use. PropellerHand is able to simulate feedback such as weight and torque by generating thrust up to 11 N in 2-DOF and a torque of 1.87 Nm in 2-DOF. Its design builds on our experience from quantitative and qualitative experiments with different form factors and parts. We evaluated our prototype through a qualitative user study in various VR scenarios that required participants to manipulate virtual objects in different ways, while changing between torques and directional forces. Results show that PropellerHand improves users’ immersion in virtual reality. Additionally, we conducted a second user study in the field of immersive visualization to investigate the potential benefits of PropellerHand there.
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    Visual Parameter Space Analysis for Classification Models
    (2019) Heyen, Frank
    We present a batch training and visualization system that enables users to visually compare different classifiers and parameter configurations in their performance and behavior. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that shows correlations between parameters and scores, as well as a training history chart. We enable users to interactively filter, highlight, colorize, sort, and group the displayed data. Using an iterative process, we developed our approach over the course of six months in cooperation with domain experts who apply machine learning for natural language processing. Our evaluation consists of two pair analytics studies and a survey with students. It demonstrates the effectiveness and usability of the implementation and shows desire to use it from domain experts, teachers and students.