# When Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design

arXiv: https://arxiv.org/abs/2610.01188 · HTML: https://mlsanity.com/paper/2610.01188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 5 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 4 of 4)

- [FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment](https://mlsanity.com/paper/2204.03646/) (2022)
- [Multi-Stage Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2401.02841/) (2024)
- [Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineering](https://mlsanity.com/paper/2505.18412/) (2025)
- [A Standardized Benchmark for Skeleton-Based Rehabilitation Assessment Using Deep Learning](https://mlsanity.com/paper/2507.21018/) (2025)

## Abstract (arXiv)

Exercise-specific joint selection can improve skeleton-based correctness classification, but what does that gain establish? We audit 1,057 repetitions from ten REHAB24-6 subjects, separating evaluation aggregation, subset structure, and temporal representation. The manual-subset kNN gain changes from 0.055 for pooled out-of-fold AUROC to 0.020 for equal-weight within-person AUROC; both paired intervals include zero. Among 1,000 dimension-matched random maps, 14 match or exceed the manual pooled result, versus 145 when bilateral structure and trunk inclusion are also matched. RBF-SVM retains a positive within-person gain, whereas logistic regression and a random-convolution comparator have negative point gains under that estimand. Sequence-order and paired-seed controls further qualify the interpretation. This exploratory audit shows why joint-selection claims require explicit estimands and structurally appropriate controls; it does not establish a new algorithm or clinical benefit.