2025/11/04 by Cai, Zhuodi, Xu, Ziyu, Pampin, Juan
Computer Science · Engineering · Psychology · #Artificial Intelligence (cs.AI) #Diversity and Impact of Dance #FOS: Computer and information sciences #Human Motion and Animation #Human Pose and Action Recognition #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Multimedia (cs.MM)
paper · doi:10.48550/arxiv.2511.02351
openalex publication_date 2025/11/04 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28
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.