一种紧凑的步态相位索引前后压力中心表示方法用于基于足底垂直地面反作用力的帕金森病分类
A Compact Stance-Indexed Anterior-Posterior COP Representation for Parkinson's Disease Classification from Plantar VGRF
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- University of Rajshahi（拉杰沙希大学）
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中文总结 AI 辅助
本研究提出紧凑的步态相位索引AP-COP表示（AP-COP10），用于帕金森病分类，在165名参与者数据上AUC达0.894，优于文献方法，并证明其保留互补判别信息。
中文摘要 AI 辅助
帕金森病会改变步态和双侧协调性，但机器学习性能也取决于连续步态信号的表示方式。本研究探讨在标准化步态相位中固定位置保留前后压力中心（AP-COP）信息是否能提供足底压力步态信号的紧凑且信息丰富的表示。使用重复的完全嵌套参与者级交叉验证，评估了来自帕金森病步态数据库中165名参与者的双侧垂直地面反作用力记录。我们提出了AP-COP10，包含五个步态窗口中的AP-COP位置和双侧不对称性。AP-COP10在相同评估流程下取得了0.894的AUC，优于三种统一的文献来源的COP表示。仅使用互补的25个非AP-COP描述符时AUC为0.856，而完整的35特征表示达到0.908。从完整表示中移除AP-COP10产生了统计学上支持的判别能力损失，而将互补描述符添加到AP-COP10仅产生微小且不支持的改进。特征竞争表明，最具信息量的步态相位索引描述符集中在早期和早中期步态相位，而源研究留出和传感器扰动分析支持了该表示的鲁棒性。这些发现表明，步态相位索引的AP-COP保留了更广泛的工程化步态描述符不易恢复的判别信息，支持用于病理步态机器学习分析的紧凑且可解释的表示。
英文摘要
Parkinson's disease alters gait and bilateral coordination, but machine-learning performance also depends on how continuous gait signals are represented. This study investigates whether preserving anterior-posterior center-of-pressure (AP-COP) information at fixed locations across normalized stance provides a compact and informative representation of plantar-force gait signals. Bilateral vertical ground reaction force recordings from 165 participants in the Gait in Parkinson's Disease Database were evaluated using repeated fully nested participant-level cross-validation. We propose AP-COP10, comprising AP-COP position and bilateral asymmetry across five stance windows. AP-COP10 achieved an AUC of 0.894 and outperformed three harmonized literature-derived COP representations under the same evaluation pipeline. The complementary 25 non-AP-COP descriptors alone achieved an AUC of 0.856, while the complete 35-feature representation achieved 0.908. Removing AP-COP10 from the complete representation produced a statistically supported loss in discrimination, whereas adding the complementary descriptors to AP-COP10 yielded only a small, unsupported improvement. Feature competition indicated that the most informative stance-indexed descriptors were concentrated in early and early-mid stance, while source-study holdout and sensor-perturbation analyses supported the robustness of the representation. These findings indicate that stance-indexed AP-COP retains discriminative information that is not readily recovered by broader engineered gait descriptors, supporting compact and interpretable representations for machine-learning analysis of pathological gait.
