Logo des Repositoriums

Auflistung nach Autor:in "Stephan, Klaus"

1 - 2 von 2
  • Workshopbeitrag
    Bitplush: Unleashing the Paws-ibilities of Smart Materials in Smart Plush Toys
    (Mensch und Computer 2023 - Workshopband, 2023) Kurze, Albrecht; Chuang, Lewis; Berger, Arne; Stephan, Klaus; Sontopski, Natalie; Hildebrandt, Stephan; Jost, Elisabeth; Eibl, Maximilian
    ‘bitplush’ is a project that will explore how individuals can be emotionally connected through smart connected plush toys. The goal is to explore and formalize the design space of implicit and multimodal interactions that can be supported with and through them, in a way that is tangible, graspable, and meaningful. To achieve this, highquality, handcrafted plush toys will be equipped with hardware and software for innovative data processing and communication. Off-the-shelf as well as novel textile-integrated and textile-based sensors and actuators will be used to develop novel communication channels that will enable a sense of “closeness over distance” without diminishing other forms of communication. Interdisciplinary tools, methods, materials and theories of social relationships serve as a framework for our design space. We conclude with a brief discussion on the planned iterative stages of this project and seek opportunities for like-minded collaborations.
  • Workshopbeitrag
    Just the Normal Fluff: The Wheel Of Plush as an Example for Human Centric Data Normalization
    (Mensch und Computer 2024 - Workshopband, 2024) Stephan, Klaus; Eibl, Maximilian; Kurze, Albrecht
    How do we translate acceleration into color or the temperature of your skin into something that feels like a heartbeat? Those questions and many more came up when we designed the Wheel of Plush, our Co-Design toolkit for smart plush toys. It features 8 sensors and 8 actuators that had to be mapped in a meaningful way so that every combination can be experienced. This article shows how we utilized normalization to answer them in a functional and easily explicable way and what we can learn from those normalization functions.