{"ID":2846114,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02351","arxiv_id":"2511.02351","title":"Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition","abstract":"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 (\u003c50 ms latency), offering a replicable framework to integrate dance-literate machines into creative, educational, and live performance contexts.","short_abstract":"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 a...","url_abs":"https://arxiv.org/abs/2511.02351","url_pdf":"https://arxiv.org/pdf/2511.02351v1","authors":"[\"Zhuodi Cai\",\"Ziyu Xu\",\"Juan Pampin\"]","published":"2025-11-04T08:15:25Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.HC\",\"cs.MM\"]","methods":"[]","has_code":false}