arXiv — cs.AI preprintsInternational7 October 2026
Adaptive Gait Biofeedback With Participant-Held-Out Modeling and Participant-Specific Updating in Chronic Ankle Instability
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.07428v1 Announce Type: new Abstract: Adaptive gait biofeedback may support repeated practice in chronic ankle instability, but its evaluation must address model performance and human response. We evaluated a temporal convolutional classifier on protocol-defined, angle-derived GOOD/BAD gait-cycle labels using participant-held-out leave-one-subject-out (LOSO) cross-validation in 20 participants. Seven participants in the adaptive-intervention group completed nine sessions over three weeks, with one motion-capture recording analyzed per session. Models updated after failed sessions wer
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets7 October 2026
- Text2Dashboard: A Governed Agent Architecture for Natural-Language Dashboard Generation over Enterprise DataBrain7 October 2026
- FluidPD: In-Place Elasticity for SLO-Aware Prefill-Decode Disaggregated LLM Serving7 October 2026
- Anchor Divergence for Semantic Geometry in Contrastive Learning7 October 2026
- RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway7 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.