Hugh Alexander von Arnim

• Rolfsjord, Sigmund Johannes Ljosvoll; Fatima, Safia; Arnim, Hugh Alexander von & Baselizadeh, Adel (2025). Multimodal Transfer Learning for Privacy in Human Activity Recognition. In Emilia, Barakova,; Ben, Allouch, Somaya; Kazuhiro, Nakadai, & Goldie, Nejat, (Ed.), Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2025. IEEE (Institute of Electrical and Electronics Engineers). p. 15¨C20. doi: 10.1109/ro-man63969.2025.11217600. Show summary

  IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed. The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.

• Arnim, Hugh Alexander von; Erdem, Cagri; C?t¨¦-Allard, Ulysse Teller Masao & Jensenius, Alexander Refsum (2025). A Sensor is not a Sensor: Diffracting the Preservation of Sonic Microinteraction with the SiFiBand. In Sei?a, Mariana & Wirfs-Brock, Jordan (Ed.), AM '25: Proceedings of the 20th International Audio Mostly Conference. Association for Computing Machinery (ACM). ISSN 9798400708183.
• Arnim, Hugh Alexander von; Kelkar, Tejaswinee & Noven, Live (2025). Motion Pointillism: The (Re/De)Construction of the Normative Body through Motion Capture. Documenta. ISSN 0771-8640. 42(1), p. 51¨C79. doi: 10.21825/documenta.93271. Full text in Research Archive Show summary

  Marker-based, optical motion capture systems make use of reflective markers, interpreting them as clusters of dimensionless points in space. Before labeling and arranging these markers, potentially to fit a model of a kinematic chain, these markers possess little referentiality to objects in physical space. However, the construction of a kinematic model of the human body requires making several assumptions about the body and its affordances. In this article, we problematize the use of the kinematic model in dance performance that employs motion capture, placing focus on the referentiality of visual representations derived from markers and models while examining how motion capture contributes to the construction of the body through the embedding of assumptions and values about what a body is and can do within the technology. Through the design and conceptualization of two interactive dance performances titled Reconfigurations and The Shapeshifter, we develop an approach to working with motion capture that we term motion pointillism, which aims to resist the systemic assumptions embedded in the modeling process. This approach conceptualizes the emergence of the dimensionless points¡¯ referentiality to a human body as a collaborative component of system development and performance, which occurs both in the design of visual representations as well as in the viewers¡¯ perception.

• Thorsen, Ola; Esema, Emmanuel Joseph; Hemaz, Said; Ellefsen, Kai Olav; Herrebr?den, Henrik & Arnim, Hugh Alexander von [Show all 7 contributors for this article] (2024). Can machine learning help reveal the competitive advantage of elite beach volleyball players? In Westphal, Florian; Peretz-Andersson, Einav; Riveiro, Maria; Bach, Kerstin & Heintz, Fredrik (Ed.), 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, J?nk?ping, Sweden. Swedish Artificial Intelligence Society. ISSN 9789180757096. p. 57¨C66. doi: 10.3384/ecp208007. Full text in Research Archive Show summary

  As the world of competitive sports increasingly embraces data-driven techniques, our research explores the potential of machine learning in distinguishing elite from semi-elite beach volleyball players. This study is motivated by the need to understand the subtle yet crucial differences in player movements that contribute to high-level performance in beach volleyball. Utilizing advanced machine learning techniques, we analyzed specific movement patterns of the motion of the torso during spikes, captured through vest-mounted accelerometers. Our approach offers novel insights into the nuanced dynamics of elite play, revealing that certain movement patterns are distinctly characteristic of higher skill levels. One of our key contributions is the ability to classify spiking movements at different skill levels with an accuracy rate as high as 87%. This current research provides a foundation of what separates elite players from their semi-elite counterparts.

• Arnim, Hugh Alexander von; Fasciani, Stefano & Erdem, Cagri (2023). The Feedback Mop Cello: An Instrument for Interacting with Acoustic Feedback Loops. In Ortiz, Miguel & Marquez-Borbon, Adnan (Ed.), Proceedings of the International Conference on New Interfaces for Musical Expression. Universidad Aut¨®noma Metropolitana. p. 494¨C499. doi: 10.5281/zenodo.11189258. Full text in Research Archive

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• Arnim, Hugh Alexander von; Fleckenstein, Abbigail Marie & Christodoulou, Anna-Maria (2025). SysMus25 Conference Proceedings. Zenodo. 164 p. doi: 10.5281/zenodo.17632991.
• Fleckenstein, Abbigail Marie; Arnim, Hugh Alexander von & Christodoulou, Anna-Maria (2025). SysMus25 Book of Abstracts. Zenodo. 165 p. doi: 10.5281/zenodo.17531650.

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• Arnim, Hugh Alexander von; Christodoulou, Anna-Maria; Burnim, Kayla; Upham, Finn; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2025). LightHearted¡ªA Framework for Mapping ECG Signals to Light Parameters in Performing Arts.
• Rolfsjord, Sigmund Johannes Ljosvoll; Arnim, Hugh Alexander von; Fatima, Safia & Baselizadeh, Adel (2025). Multimodal Transfer Learning for Privacy in Human Activity Recognition.
• Arnim, Hugh Alexander von; Erdem, Cagri; C?t¨¦-Allard, Ulysse Teller Masao & Jensenius, Alexander Refsum (2025). A Sensor is not a Sensor: Diffracting the Preservation of Sonic Microinteraction with the SiFiBand.
• Esterhazy, Rachelle; Arnim, Hugh Alexander von & Damsa, Crina I. (2025). Multimodal learning analytics to explore key moments of interdisciplinary knowledge-construction.
• Sanchez, Daniel; Damsa, Crina I.; Esterhazy, Rachelle & Arnim, Hugh Alexander von (2024). Sense-making and Use of Multimodal Feedback in Team-based Simulations in Nursing Education. EARLI-SIG27 Conference.
• Esterhazy, Rachelle; Arnim, Hugh Alexander von & Damsa, Crina I. (2024). Multimodal learning analytics to explore key moments of interdisciplinary knowledge-construction.
• Arnim, Hugh Alexander von & Kelkar, Tejaswinee (2024). The Shapeshifter: Motion Capture and Interactive Dance for Co-constructing the Body.
• Arnim, Hugh Alexander von; Fasciani, Stefano & Erdem, Cagri (2023). The Feedback Mop Cello: An Instrument for Interacting with Acoustic Feedback Loops.

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