发表机构
University of Science and Technology of China; Shanghai Jiao Tong University; Southwest Jiaotong University（中国科学技术大学; 上海交通大学; 西南交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
审计发现特定动作关节选择对骨架正确性分类的增益依赖于评估聚合和子集结构，需明确估计目标与适当对照，未确立新算法或临床益处。
AI 中文摘要
特定动作的关节选择可以改善基于骨架的正确性分类，但这种增益究竟证明了什么？我们审计了来自十名REHAB24-6受试者的1,057次重复动作，将评估聚合、子集结构和时间表示分开考虑。手动子集kNN的增益从池化折外AUROC的0.055变为等权人内AUROC的0.020；两个配对区间均包含零。在1,000个维度匹配的随机映射中，14个达到或超过手动池化结果，而当双侧结构和躯干包含也被匹配时，这一数字为145。RBF-SVM在人内增益上保持正值，而逻辑回归和随机卷积比较器在该估计目标下具有负的点增益。序列顺序和配对种子控制进一步限定了解释。这项探索性审计表明，关节选择的主张需要明确的估计目标和结构上适当的对照；它并未确立新算法或临床益处。
英文摘要
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.
CommentsExploratory offline audit of subject-disjoint skeleton-based exercise correctness evaluation; 5 pages, 2 figures, 3 tables
