Gao, Lei;
Tokuda, Yutaka;
Bansal, Shubhi;
Subramanian, Sriram;
(2024)
Computational Gastronomy and Eating with Acoustophoresis.
In:
ICMI Companion '24: Companion Proceedings of the 26th International Conference on Multimodal Interaction.
(pp. pp. 113-116).
ACM
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Abstract
Technology-driven human-food interaction has gained increasing attention and importance from researchers. Digital food fabrication and computational food experiences have introduced greater technical challenges. Recent advances in acoustophoresis, which uses ultrasound to contactlessly manipulate solid and liquid materials for multisensory experiences, show great potential for computational food fabrication and human-food interaction. This work discusses the potential opportunities and challenges of acoustophoretic technology in the Human-Food Interaction (HFI) domain. We envisage that acoustophoretic technology can serve as a versatile platform for emerging computational gastronomy and eating, providing promising new technical solutions with diverse food processing functionalities without requiring multiple specialized kitchen equipment and large spaces, and enhancing the interactivity possibilities in different eating scenarios. We aim to inspire more innovative research ideas and enrich the perspectives on computation and interaction in HFI.
| Type: | Proceedings paper |
|---|---|
| Title: | Computational Gastronomy and Eating with Acoustophoresis |
| Event: | ICMI '24: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3686215.3686218 |
| Publisher version: | http://dx.doi.org/10.1145/3686215.3686218 |
| Language: | English |
| Additional information: | Copyright © 2024 Owner/Author. This work is licensed under a Creative Commons Attribution International 4.0 License. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10201040 |
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