arXiv (All)
open access
FenceXR: AR Movement Replay for Error-Detection Training and Spatially Grounded Feedback
Recognizing technical errors in movement is a perceptual skill important to motor learning, but it is challenging for beginners in fast, complex sports like fencing to develop it. A coach's attention is scarce, live demonstrations vary from repetition to repetition, and video review is limited to wh
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
- Memorize, Adapt, Ignore: Diagnosing Robot Learning Mechanisms under Training Data Variation#1143002
- Inhibition in motion: Test-retest reliability of inhibitory kinematics in a go/no-go mouse tracking task#266294
- Why motor learning involves multiple systems: an algorithmic perspective#273229
- Dyadic Human Throwing: Data Collection, Analysis, and Implications for Robotic Systems#291214
- Combined Action Observation and Motor Imagery Elicits Superior Frontoparietal Activation in Elite Ski Jumpers: An fNIRS Study#314707
Record · ID 1014099
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