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

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

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2610.01188 [cs.AI]

(or arXiv:2610.01188v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.01188 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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