DiaSeg：从DTW路径中提取对角段用于可解释步态分析
DiaSeg: Diagonal Segment Extraction from DTW Paths for Interpretable Gait Analysis
- Université Bourgogne Europe（勃艮第欧洲大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
DiaSeg从DTW路径提取对角段，用五个几何特征实现无监督步态模式发现，在91名受试者六种临床状况上验证，区分病理准确率达75%，结合周期特征达91.7%，提供相位级可解释性。
AI中文摘要：
动态时间规整（DTW）是衡量时间序列相似性的主流方法，然而标准做法在计算单个距离值后便丢弃最优规整路径，丢失了与临床诊断最相关的局部对齐信息。我们提出DiaSeg，一个从DTW路径中提取具有受控间断的对角段的框架，通过五个几何特征（有效长度、中断次数、成本变化、时间位置和路径上下文）对每个段进行表征，并在无需特定领域特征工程的情况下实现无监督模式发现。在涵盖六种临床状况（健康老龄化、帕金森病、亨廷顿病、肌萎缩侧索硬化症、脑肿瘤和中风）的91名受试者上验证后，得出三项发现。第一，对角段形成与生物力学相位注释一致的一致无监督模式（轮廓系数0.33），基于标签的验证确认健康和病理步态近乎完美的分离（ARI最高达0.986）。第二，这些段以69%（监督）和75%（患者级聚类）的准确率区分病理，病理通过段长度的分布变化显现；将段级和周期级特征结合可进一步提高分类准确率至91.7%。第三，虽然基于周期的方法达到更高准确率（91%），对角段提供了全局表示所不具备的相位特异性可解释性，定位步态周期内协调性破坏的位置。因此，DiaSeg将DTW从黑盒距离转变为神经退行性疾病评估的可解释时间特征来源。
英文摘要：
Dynamic Time Warping (DTW) is the dominant approach for measuring similarity between time series, yet standard practice discards the optimal warping path after computing a single distance value, losing local alignment information most relevant to clinical diagnosis. We introduce DiaSeg, a framework that extracts diagonal segments from DTW paths with controlled breaks, characterizing each segment by five geometric features (effective length, interruption count, cost variation, temporal position, and path context), and enabling unsupervised pattern discovery without domain-specific feature engineering. Validated on 91 subjects across six clinical conditions (healthy aging, Parkinson's, Huntington's, ALS, brain tumor, and stroke), three findings emerge. First, diagonal segments form consistent unsupervised patterns (silhouette 0.33) aligned with biomechanical phase annotations, with label-based validation confirming near-perfect separation of healthy and pathological gait (ARI up to 0.986). Second, segments discriminate pathology at 69% (supervised) and 75% (patient-level clustering), with pathology manifesting through distributional shifts in segment length; combining segment and cycle-level features further improves classification to 91.7%. Third, while cycle-based methods achieve higher accuracy (91%), diagonal segments provide phase-specific interpretability unavailable in global representations, localizing where coordination breaks down within the gait cycle. DiaSeg thus transforms DTW from a black-box distance into a source of interpretable temporal features for neurodegenerative disease assessment.
