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Auflistung nach Autor:in "Weidemann, Alexandra"

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  • Konferenzbeitrag
    Investigation of frustration
    (Mensch und Computer 2019 - Tagungsband, 2019) Weidemann, Alexandra; Rußwinkel, Nele
    Frustration often occur in an interaction with a technical device. It has a strong impact on the quality of interaction and should be carefully considered in planning how humans and machines should interact with each other. In this study, the measurability of frustration with subjective (questionnaires) and objective (electrocardiogram and electromyogram) methods and the influence on user state factors (like dominance, self-confidence, and performance) in an emotion induction task was examined. The sample size was 20 participants. Here we show that the induced frustration could be measured with questionnaires. Furthermore, positive correlations between frustration and mental demand and self-reported performance, as well as negative correlations between frustration and dominance and self-confidence were observed. The psychophysiological methods also indicate differences between the frustrated group (FRUST-group) and the control group (NOFRUST-group). Even if the frustration is only psychologically perceived, it should be determined how frustration could be detected and minimized. The information and the knowledge about underlying processes and relationships could be used for a better human-machine interaction.
  • Textdokument
    Partizipatives Entwerfen zukünftiger Roboter
    (INFORMATIK 2017, 2017) Reichel, Sebastian; Weidemann, Alexandra; Compagna, Diego; Deimel, Raphael
    Im interdisziplinären Forschungsprojekt MTI-engAge wird an vier verschiedenartigen Robotern untersucht, wie Aussehen, soziale Kommunikationsfunktionalität, Größe, Bewegung und Verhalten das Gelingen einer Interaktion zwischen Mensch und Roboter-Assistenzsystemen beeinflussen, indem gemeinsam mit Nutzern praxisorientierte Demonstratoren evaluiert werden. Die Einbeziehung der Nutzer in Entwicklungsprozesse von Systemen und die gründliche Auseinandersetzung mit ihren mentalen Modellen werden als Schlüssel für die Akzeptanz und damit für den Erfolg zukünftiger Roboter angesehen.