发表机构
University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究为MoCha 2026帕金森步态挑战赛提出方案，通过受试者级后验聚合等技术，在冻结SMPL运动编码器上取得宏F1值0.6945，获58支参赛队第一，验证了该类任务中聚合策略的关键作用。
AI 中文摘要
我们介绍了MoCha 2026帕金森步态基准与挑战赛的获胜方案，该方案基于临床站点采集的、训练时未见过的标准化SMPL运动数据预测MDS-UPDRS步态严重度。该系统在隐藏测试集上达到0.6945的宏F1值，在冻结的公共运动编码器上仅使用单个4×512线性层，在58个参赛方案中排名第一，领先第二名的0.5807和主办方基线的0.4289。几乎所有优势都来自三个通常被视为常规操作的阶段：复现参考基准的精确头部结构、在主办方提供的受试者分组内对每步后验进行平均、以及对特征均值和决策操作点的无标签直推式校准。对编码器进行微调会在四个不同方面表现更差，而10种替代编码器的效果也更差。所有 ablation 结果均来自隐藏测试集，因为我们自身的留两队列交叉验证在11种配置上与决定得分呈负相关。我们完整给出了负向结果，并确定最大增益来源——受试者级聚合是该基准的性能上限。
英文摘要
We describe the winning entry to the MoCha 2026 Benchmark and Challenge on Parkinsonian Gait, which predicts MDS-UPDRS gait severity from canonicalized SMPL motion recorded at clinical sites unseen during training. The system reaches 0.6945 macro-F1 on the hidden test and ranked first of 58 entries, ahead of the runner-up at 0.5807 and the organizers' baseline at 0.4289, on a frozen public motion encoder with a single $4\times512$ linear layer. Nearly all of the margin comes from three stages usually treated as bookkeeping: reproducing the reference benchmark's exact head recipe, averaging per-walk posteriors within the subject grouping the organizers ship, and a label-free transductive calibration of the feature mean and the decision operating point. Fine-tuning the encoder lost in four distinct forms, and ten alternative encoders were worse. Every ablation number is a paid read on the hidden test, because our own leave-two-cohort-out cross-validation proved anti-correlated with the deciding score over eleven configurations. We give the negative record in full, and identify our largest gain, subject-level aggregation, as the binding ceiling on this benchmark.
