Wei, Xijia;
Fang, Yuan;
Chetty, Kevin;
Cho, Youngjun;
Bianchi-Berthouze, Nadia;
(2025)
Vomme: A Multimodal Sensing Platform for Video, Audio, mmWave and Skeleton Data Capturing.
In:
ANAI '25: Proceedings of the 2025 ACM Workshop on Access Networks with Artificial Intelligence.
(pp. pp. 36-40).
Association for Computing Machinery (ACM): New York, NY, USA.
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Abstract
mmWave sensing has offered a non-intrusive opportunity for human behaviour recognition. However, current mmWave sensing platform is limited for raw signal acquisition together with time-aligned multimedia data recording functions. In addition, there is a lack of open-sourced solution multi-mmWave sensor capturing toolbox. In this paper, we introduce Vomme, a multimodal sensing platform for video, audio, mmWave, and RBG-extracted skeleton data capturing. Vomme supports a series of sensor combination setup for data capturing, demonstrating potentials to be deployed under various application scenarios. Vomme synchronizes multimodal signals via the host computer's timestamp. Hardware-level synchronization is also supported by integrating a micro controller for precise sampling frequency control and avoiding the inter-sensor interference when using multiple mmWave sensors. Vomme is fully publicly open-sourced.
| Type: | Proceedings paper |
|---|---|
| Title: | Vomme: A Multimodal Sensing Platform for Video, Audio, mmWave and Skeleton Data Capturing |
| Event: | 2025 ACM Workshop on Access Networks with Artificial Intelligence (ANAI '25) |
| Location: | Hong Kong, China |
| Dates: | 4 Nov 2025 - 8 Nov 2025 |
| ISBN-13: | 9798400719813 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3737904.3768536 |
| Publisher version: | https://doi.org/10.1145/3737904.3768536 |
| Language: | English |
| Additional information: | This is an Open Access article published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | mmWave Sensing, Human Activity Recognition, Multimodal Motion Capture |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10218213 |
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