Skill Profiling with Attributable Reasoning (SPAR): A Wearable Analysis System for Boxing

arXiv:2609.30753 · cs.HC, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.HC, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Skill Profiling with Attributable Reasoning — 23× in this paper
• AUC — 10× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Calibration — 7× in this paper · explained in 7 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accelerometer Data — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Contrastive Ablation Attribution — 2× in this paper
• Directed Counterfactual Search — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• MOMENT: A Family of Open Time-series Foundation Models
• RISE: Randomized Input Sampling for Explanation of Black-box Models
• Inertia-1: An Open Exploration of Wearable Motion Foundation Models
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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