Datensatz Zitierlink: 
http://hdl.handle.net/20.500.12708/193227
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Titel: 
Hand-in-Hand: Investigating Mechanical Tracking for User Identification in Cobot Interaction
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Zitat: 
Saad, A., Pascher, M., Kassem, K., Heger, R., Liebers, J., Schneegass, S., & Gruenefeld, U. (2023). Hand-in-Hand: Investigating Mechanical Tracking for User Identification in Cobot Interaction. In F. Michahelles, P. Knierim, & J. Häkkilä (Eds.), MUM ’23: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia (pp. 1–9). Association for Computing Machinery. https://doi.org/10.1145/3626705.3627771
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Verlags-DOI: 
10.1145/3626705.3627771
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Publikationstyp: 
Konferenzbeitrag - Full-Paper Contribution
de
Sprache: 
Englisch
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Autor_innen: 
Saad, Alia 
Pascher, Max 
Kassem, Khaled 
Heger, Roman 
Liebers, Jonathan 
Schneegass, Stefan 
Gruenefeld, Uwe 
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Organisations­einheit: 
E193-04 - Forschungsbereich Artifact-based Computing & User Research 
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Erschienen in: 
MUM '23: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia 
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ISBN: 
979-8-4007-0921-0
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Datum (veröffentlicht): 
2023
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Veranstaltungs­name: 
22th International Conference on Mobile and Ubiquitous Multimedia (MUM 2023) 
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Veranstaltungs­zeitraum: 
3-Dez-2023 - 6-Dez-2023
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Veranstaltungs­ort: 
Vienna, Österreich
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Umfang: 
9
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Verlag: 
Association for Computing Machinery, New York
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Keywords: 
behavioral biometrics; cobots; human-robot collaboration; human-robot interaction
en
Abstract: 
Robots play a vital role in modern automation, with applications in manufacturing and healthcare. Collaborative robots integrate human and robot movements. Therefore, it is essential to ensure that interactions involve qualified, and thus identified, individuals. This study delves into a new approach: identifying individuals through robot arm movements. Different from previous methods, users guide the robot, and the robot senses the movements via joint sensors. We asked 18 participants to perform six gestures, revealing the potential use as unique behavioral traits or biometrics, achieving F1-score up to 0.87, which suggests direct robot interactions as a promising avenue for implicit and explicit user identification.
en
Forschungs­schwerpunkte: 
Visual Computing and Human-Centered Technology: 50%
Automation and Robotics: 20%
Computational System Design: 30%
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Wissenschafts­zweig: 
1020 - Informatik: 90%
1010 - Mathematik: 10%
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Enthalten in den Sammlungen:
Conference Paper

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