Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation

arXiv:2607.04842 · cs.LG · Submitted 2026-07-06 · Read on arXiv

cs.LG

Submitted: 2026-07-06

Updated: 2026-09-11

Journal ref: MDPI AI 2026

DOI: 10.3390/ai7090356

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Functional Movement Screen — 3× in this paper
• Deep Squat — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Annotation Errors — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• Generation of the Automatically Generated Label Distributions — 1× in this paper
• Hurdle Step — 1× in this paper
• Resisted Gait Simulation — 1× in this paper

Sources

• Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks