Meng Lin, Zhang Xiaofei, Shi Yu, Li Xinge, Pang Jun, Chen Lei, Zhu Xiaodong, Xu Rui, Ming Dong
IEEE Trans Neural Syst Rehabil Eng. 2025;33:687-695. doi: 10.1109/TNSRE.2025.3535696. Epub 2025 Feb 13.
As one of the main motor indicators of Parkinson's disease (PD), postural instability and gait disorder (PIGD) might manifest in various but subtle symptoms at early stage resulting in relatively high misdiagnosis rate. Quantitative gait assessment under dual task or complex motor task (i.e., turning) may contribute to better understanding of PIGD and provide a better diagnostic indicator of early-stage PD. However, few studies have explored gait deviation evaluation algorithms under a complex dual task that reflect disease specificity. In this work, we proposed a novel inertial-based gait normalcy index (GNI) based on inertial-based quantitative gait assessment model to characterize the overall gait performance during both straight walking and turning with or without serial-3 subtraction task. The factor of group and task on the GNI variable was investigated and the feasibility of GNI to improve early-stage PD diagnostic performance was validated. The experimental results showed that the task paradigm is a significant factor on GNI performance where the dual-task GNI at turn had the best discriminating ability between early PD and HC (AUC =0.992) and was significantly correlated with UPDRS III (r =0.81), MMSE(r =0.57) and Mini-BEST(r =0.65). We also observed that the turning-based GNI has larger effect size compared to clinical scales, demonstrating that GNI during turning can reflect the changes of functional mobility in rehabilitation for the early PD. Our work offers an innovative and potential gait biomarker for early-stage PD diagnostics and provides a new perspective into gait performance of complex dual task and its application in early PD.
作为帕金森病（PD）的主要运动指标之一，姿势不稳和步态障碍（PIGD）在早期可能表现为各种细微症状，导致误诊率相对较高。在双重任务或复杂运动任务（即转弯）下进行定量步态评估，可能有助于更好地理解PIGD，并为早期PD提供更好的诊断指标。然而，很少有研究探索反映疾病特异性的复杂双重任务下的步态偏差评估算法。在这项工作中，我们基于基于惯性的定量步态评估模型，提出了一种新颖的基于惯性的步态正常指数（GNI），以表征在执行或不执行连续减3任务的直线行走和转弯过程中的整体步态表现。研究了组和任务对GNI变量的影响，并验证了GNI改善早期PD诊断性能的可行性。实验结果表明，任务范式是影响GNI性能的一个重要因素，其中转弯时的双重任务GNI在区分早期PD和健康对照（HC）方面具有最佳辨别能力（AUC = 0.992），并且与统一帕金森病评定量表第三部分（UPDRS III）（r = 0.81）、简易精神状态检查表（MMSE）（r = 0.57）和简短伯格平衡量表（Mini-BEST）（r = 0.65）显著相关。我们还观察到，与临床量表相比，基于转弯的GNI具有更大的效应量，表明转弯时的GNI可以反映早期PD康复中功能移动性的变化。我们的工作为早期PD诊断提供了一种创新且有潜力的步态生物标志物，并为复杂双重任务的步态表现及其在早期PD中的应用提供了新的视角。
