Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908626884493312 |
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| author | Cai, Zhuodi Xu, Ziyu Pampin, Juan |
| author_facet | Cai, Zhuodi Xu, Ziyu Pampin, Juan |
| contents | We introduce a lightweight, real-time motion recognition system that enables synergic human-machine performance through wearable IMU sensor data, MiniRocket time-series classification, and responsive multimedia control. By mapping dancer-specific movement to sound through somatic memory and association, we propose an alternative approach to human-machine collaboration, one that preserves the expressive depth of the performing body while leveraging machine learning for attentive observation and responsiveness. We demonstrate that this human-centered design reliably supports high accuracy classification (<50 ms latency), offering a replicable framework to integrate dance-literate machines into creative, educational, and live performance contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition Cai, Zhuodi Xu, Ziyu Pampin, Juan Machine Learning Artificial Intelligence Human-Computer Interaction Multimedia We introduce a lightweight, real-time motion recognition system that enables synergic human-machine performance through wearable IMU sensor data, MiniRocket time-series classification, and responsive multimedia control. By mapping dancer-specific movement to sound through somatic memory and association, we propose an alternative approach to human-machine collaboration, one that preserves the expressive depth of the performing body while leveraging machine learning for attentive observation and responsiveness. We demonstrate that this human-centered design reliably supports high accuracy classification (<50 ms latency), offering a replicable framework to integrate dance-literate machines into creative, educational, and live performance contexts. |
| title | Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction Multimedia |
| url | https://arxiv.org/abs/2511.02351 |