When Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design
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...
| Authors | Haotian Chen, Jingkun Yu, Yuning Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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