Hugh Alexander von Arnim

• Hugh Alexander von Arnim, Finn Upham & Alexander Refsum Jensenius (2026). Exploring Methods for Measuring Audience Body Motion in Concert Halls. In Ivica Ico Bukvic (Eds.), Proceedings of the Sound and Music Computing Conference 2026. SMC Network. Full text in Research Archive Show summary

  Measurement of audience members’ body motion in ecological concert settings must navigate environmental constraints, access to audience members, privacy considerations, and behavioural norms of the performance event. This study compares five distinct technologies for capturing motion from “audience member” participants under simulated symphonic concert conditions: visible-range imaging (RGB), near-infrared imaging (IR), thermal imaging, thermal data, and torso-mounted accelerometry. Seven quantity of motion (QoM) time series were generated from these measurements, each cleaned and normalised to reveal similarities and differences in the information they provide regarding four concert-typical behaviours: Large Actions such as posture changes, Applause, Stillness, and Respiration. Results show comparable, though not identical, quantity of motion measurements across devices in this simulated concert setting. We discuss the advantages and disadvantages of the various signals, share recommendations on key measurement parameters, and discuss scaling these motion capture methods for larger audiences in real concert halls.

• Hugh Alexander von Arnim, Anna-Maria Christodoulou, Kayla Burnim, Finn Upham, Tejaswinee Kelkar & Alexander Refsum Jensenius (2026). LightHearted—A Framework for Mapping ECG Signals to Light Parameters in Performing Arts. In Anthony L. Brooks (Eds.), Proceedings of the 14th EAI International Conference on ArtsIT, Interactivity and Game Creation, ArtsIT 2025. Springer. ISSN 9783032269997. p. 719–738. doi: 10.1007/978-3-032-26999-7_18. Full text in Research Archive Show summary

  This paper presents LightHearted, an open-source Python framework for mapping the heart’s electrical activity from electrocardiography (ECG) signals acquired from performers to stage lighting in a concert setting. The aim is to provide dynamic lighting coupled to the physiological processes occurring during performance. Drawing upon critical approaches towards using biosignals in interactive systems and live concert research, we outline the framework’s design and implementation and present a case study with the Aarhus Symphony Orchestra. This paper highlights both technical and conceptual challenges of integrating biosensor-based lighting into a large-scale orchestral context. Results from a post-concert audience survey (N = 324) suggest that while responses to the lighting were mixed, it was generally not perceived as distracting and would be welcomed for use in future concerts.

• Anna-Maria Christodoulou, Hugh Alexander von Arnim & Alexander Refsum Jensenius (2025). Supporting Narrative Comprehension in Programmatic Music through Music and Light. In Angela McArthur, Emma-Kate Matthews & Tom Holberton (Ed.), Proceedings of the 17th International Symposium on Computer Music Multidisciplinary Research. The Laboratory PRISM “Perception, Representations, Image, Sound, Music”. ISSN 9791097498061. p. 447–454. doi: 10.5281/zenodo.17496754. Full text in Research Archive Show summary

  Programmatic music, such as Tchaikovsky’s Overture Romeo and Juliet, relies on the audience’s ability to associate musical motifs with narrative elements. This is a demanding task for less experienced listeners, particularly when cues are subtle, such as those conveyed through timbre. This paper explores how dynamic stage lighting, driven by physiological signals, can enhance narrative comprehension in orchestral performance. Using the LightHearted interactive lighting system, different characters of the Overture were mapped to distinct colored lights, whose intensities were dynamically modulated in real time by the heart rates of the conductor and selected musicians. This integration aimed to convey subtle narrative cues to the audience in real time. Audience feedback suggests that this approach not only clarifies musical narratives but also enhances the overall experience.

• Sigmund Johannes Ljosvoll Rolfsjord, Safia Fatima, Hugh Alexander von Arnim & Adel Baselizadeh (2025). Multimodal Transfer Learning for Privacy in Human Activity Recognition. In Barakova, Emilia, Allouch, Somaya Ben, Nakadai, Kazuhiro & Nejat, Goldie (Ed.), Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2025. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331587710. p. 15–20. doi: 10.1109/ro-man63969.2025.11217600. Full text in Research Archive 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.

• Hugh Alexander von Arnim, Cagri Erdem, Ulysse Teller Masao Côté-Allard & Alexander Refsum Jensenius (2025). A Sensor is not a Sensor: Diffracting the Preservation of Sonic Microinteraction with the SiFiBand. In Mariana Seiça & Jordan Wirfs-Brock (Ed.), AM '25: Proceedings of the 20th International Audio Mostly Conference. Association for Computing Machinery (ACM). ISSN 9798400720659. p. 318–325. doi: 10.1145/3771594.3771626. Full text in Research Archive Show summary

  This paper documents our exploratory work to preserve the interactive music system Stillness Under Tension—developed to explore inverse sonic microinteraction—by porting it from the original and discontinued Myo sensor armband to SiFiBand, a new prototype armband with motion (IMU) and muscle (EMG) sensors. We approach this by merging the Multilevel Dynamic Preservation model with a “diffraction-in-action” method grounded in a theoretical entanglement perspective. Rather than focusing on the Myo version’s artefactual remains, we explore the difference in data representations offered by the two devices as our point of departure. The paper describes the sensor devices, evaluating their data representations given their technical specifications, and describing how these differences propagate throughout our attempt to preserve the system, enacting necessary changes. We discuss the implications of merging these methods in view of the long-term preservation of interactive music systems. Our version 2.0 of Stillness Under Tension finds itself experientially in a position between familiarity and newness.

• Hugh Alexander von Arnim, Tejaswinee Kelkar & Live Noven (2025). Motion Pointillism: The (Re/De)Construction of the Normative Body through Motion Capture. Documenta: tijdschrift voor theater. ISSN 0771-8640. 42(1), p. 51–79. 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.

• Ola Thorsen, Emmanuel Joseph Esema, Said Hemaz, Kai Olav Ellefsen, Henrik Herrebrøden & Hugh Alexander von Arnim [Show all 7 contributors for this article] (2024). Can machine learning help reveal the competitive advantage of elite beach volleyball players? In Florian Westphal, Einav Peretz-Andersson, Maria Riveiro, Kerstin Bach & Fredrik Heintz (Ed.), 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, Jönköping, Sweden. Swedish Artificial Intelligence Society. ISSN 9789180757096. p. 57–66. 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.

• Hugh Alexander von Arnim, Stefano Fasciani & Cagri Erdem (2023). The Feedback Mop Cello: An Instrument for Interacting with Acoustic Feedback Loops. In Miguel Ortiz & Adnan Marquez-Borbon (Ed.), Proceedings of the International Conference on New Interfaces for Musical Expression. Universidad Autónoma Metropolitana. p. 494–499. doi: 10.5281/zenodo.11189258. Full text in Research Archive Show summary

  This paper presents the Feedback Mop Cello, an instrument integrating acoustic feedback loops generated through a microphone and loudspeaker in combination with a control interface inspired by the cello. Current paradigms of interaction with feedback instruments are based around ideas of negotiation with autonomous systems rather than control. We explore the possibility of integration of negotiated and controlled elements through a design focused on isolating the acoustic feedback loop signal path from the signal path to which sound processing is applied. We focus on three musical parameters of timbre, pitch, and dynamics. We present timbre as a parameter to mainly be negotiated within the feedback loop, while pitch and dynamics are parameters that can be explicitly controlled through the interface. An approach is taken to minimize components within the feedback loop in order to foreground the choice of the loudspeaker as an integral part of the instrument’s sound. A preliminary user study is carried out involving five semiprofessional musicians, focusing on their reflection regarding their interaction with the acoustic feedback loop.

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

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• Alexander Refsum Jensenius, Bálint Laczkó, Poutaraud, Joachim, Widmer, Marcus, Furmyr, Frida & Jinyue Guo [Show all 9 contributors for this article] (2026). musicalgestures: A Python toolbox for visualising and analysing music-related video recordings. doi: 10.5281/zenodo.22287338. Full text in Research Archive Show summary

  Software. This is the Python version of the Musical Gestures Toolbox, containing a complete solution for loading different types of video recordings (including 360-degree videos). It was originally built to analyse music-related videos, but it works well for many other applications. It includes a large collection of tools for visualising human body motion, including average images, motion images, motion history images, motiongrams, and videograms. It also includes spatial–temporal tools for creating motion history videos. Finally, it wraps different state-of-the-art pose estimation methods (CV2 and MediaPipe) and derives various quantity-of-motion measures.

• Abbigail Marie Fleckenstein, Hugh Alexander von Arnim & Anna-Maria Christodoulou (2025). Editorial Note. In Hugh Alexander von Arnim, Abbigail Marie Fleckenstein & Anna-Maria Christodoulou (Ed.), SysMus25 Conference Proceedings. Zenodo. p. 5–5. Full text in Research Archive
• Anna-Maria Christodoulou, Hugh Alexander von Arnim & Alexander Refsum Jensenius (2025). Supporting Narrative Comprehension in Programmatic Music through Music and Light. Full text in Research Archive
• Hugh Alexander von Arnim, Anna-Maria Christodoulou, Kayla Burnim, Finn Upham, Tejaswinee Kelkar & Alexander Refsum Jensenius (2025). LightHearted—A Framework for Mapping ECG Signals to Light Parameters in Performing Arts. Full text in Research Archive
• Sigmund Johannes Ljosvoll Rolfsjord, Hugh Alexander von Arnim, Safia Fatima & Adel Baselizadeh (2025). Multimodal Transfer Learning for Privacy in Human Activity Recognition. Full text in Research Archive
• Hugh Alexander von Arnim, Cagri Erdem, Ulysse Teller Masao Côté-Allard & Alexander Refsum Jensenius (2025). A Sensor is not a Sensor: Diffracting the Preservation of Sonic Microinteraction with the SiFiBand. Full text in Research Archive Show summary

  This paper documents our exploratory work to preserve the interactive music system Stillness Under Tension—developed to explore inverse sonic microinteraction—by porting it from the original and discontinued Myo sensor armband to SiFiBand, a new prototype armband with motion (IMU) and muscle (EMG) sensors. We approach this by merging the Multilevel Dynamic Preservation model with a “diffraction-in-action” method grounded in a theoretical entanglement perspective. Rather than focusing on the Myo version’s artefactual remains, we explore the difference in data representations offered by the two devices as our point of departure. The paper describes the sensor devices, evaluating their data representations given their technical specifications, and describing how these differences propagate throughout our attempt to preserve the system, enacting necessary changes. We discuss the implications of merging these methods in view of the long-term preservation of interactive music systems. Our version 2.0 of Stillness Under Tension finds itself experientially in a position between familiarity and newness.

• Rachelle Esterhazy, Hugh Alexander von Arnim & Crina I. Damsa (2025). Multimodal learning analytics to explore key moments of interdisciplinary knowledge-construction. Full text in Research Archive
• Daniel Sanchez, Crina I. Damsa, Rachelle Esterhazy & Hugh Alexander von Arnim (2024). Sense-making and Use of Multimodal Feedback in Team-based Simulations in Nursing Education. EARLI-SIG27 Conference. Full text in Research Archive
• Rachelle Esterhazy, Hugh Alexander von Arnim & Crina I. Damsa (2024). Multimodal learning analytics to explore key moments of interdisciplinary knowledge-construction. Full text in Research Archive
• Hugh Alexander von Arnim & Tejaswinee Kelkar (2024). The Shapeshifter: Motion Capture and Interactive Dance for Co-constructing the Body. Full text in Research Archive
• Hugh Alexander von Arnim, Stefano Fasciani & Cagri Erdem (2023). The Feedback Mop Cello: An Instrument for Interacting with Acoustic Feedback Loops. Full text in Research Archive

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