arXiv (All)
open access
SAGE-Yoga: Multi-Cue Learning for Yoga Pose Classification and Joint-Level Correction
Automated yoga analysis requires both accurate pose classification and interpretable feedback on pose execution. However, existing methods often rely on a single visual prediction, struggle to distinguish visually similar poses, and treat pose classification and correction as separate tasks. To addr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multimodal EEG, ECG, and video dataset of yoga practitioners during concentration and mind-wandering tasks.#275337
- Exploring neural mechanisms of Mandarin tone sandhi perception via fNIRS: the role of gesture in multimodal integration.#160037
- Skel-WAM: A Hand-Skeleton-Conditioned World Action Model for Human-to-Robot Manipulation Transfer#1035913
- Motionscope AI: comprehensive human activity recognition through integrated pose analysis and temporal modeling#293278
- De-biasing Skeleton-based Action Recognition with Convex Hull Adaptive Shift#1116785
Record · ID 1034013
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive