Kribus-Shmiel Lotem, Zeilig Gabi, Sokolovski Boris, Plotnik Meir
Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Tel Hashomer, Israel.
Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel.
PLoS One. 2018 Feb 8;13(2):e0192049. doi: 10.1371/journal.pone.0192049. eCollection 2018.
The Phase coordination index (PCI), a temporal gait measure that quantifies consistency and accuracy in generating the anti-phased left-right stepping pattern, assesses bilateral coordination of gait in various cohorts (e.g., Parkinson's disease, post stroke). As PCI is based on mean values calculated across a series of gait cycles, individuals are required to perform lengthy walking trials, prolonging gait assessments which cause discomfort to some of them. This study introduces an algorithm to identify the required number of strides to obtain a reliable, characteristic PCI value.
Simulated data sets, as well as physiological data (obtained from healthy elderly and young persons, from over ground and treadmill trials) were used in this research. A series of N-1 PCI values was calculated for i = 2,3,4…N gait cycles for each participant. There is a value i = k, representing certain number of cycles, for which no significant change in PCI occurs as additional cycles are added, termed point of stabilization (POS). The algorithm presented here uses a 2-stage iterative process to determine POS. Stage 1 searches for the gross location of the interval of PCI values containing the POS. In stage 2, the algorithm performs a high-resolution recursive, iterative process within this interval to find the exact point. The criterion for defining stability within a window of PCI values is a coefficient of variation (CV) of ≤ 5%.
Our recursive, iterative algorithm indicates that ~23 strides on average should be captured to attain a characteristic PCI.
Gait trials with at least 23 strides on average should suffice to obtain a reliable estimation of PCI in healthy young adults. While this methodology may be considered generic, future studies should obtain POS values based on additional cohorts (e.g., disabled participants, fixed walking speeds).
相位协调指数（PCI）是一种步态时间测量指标，用于量化产生反相左右步幅模式的一致性和准确性，可评估不同人群（如帕金森病患者、中风后患者）的步态双侧协调性。由于PCI基于一系列步态周期的平均值计算得出，因此个体需要进行长时间的步行试验，这延长了步态评估时间，给部分人带来不适。本研究引入一种算法，以确定获得可靠的特征性PCI值所需的步数。
本研究使用了模拟数据集以及生理数据（来自健康老年人和年轻人，包括地面行走和跑步机试验）。为每位参与者在i = 2、3、4…N个步态周期中计算一系列N - 1个PCI值。存在一个值i = k，表示特定数量的周期，当添加更多周期时，PCI不会发生显著变化，称为稳定点（POS）。这里提出的算法使用两阶段迭代过程来确定POS。第一阶段搜索包含POS的PCI值区间的大致位置。在第二阶段，算法在该区间内执行高分辨率递归迭代过程以找到精确点。定义PCI值窗口内稳定性的标准是变异系数（CV）≤5%。
我们的递归迭代算法表明，平均捕获约23步应足以获得特征性PCI。
对于健康的年轻人，平均至少23步的步态试验应足以获得可靠的PCI估计值。虽然该方法可能被认为具有通用性，但未来研究应基于其他人群（如残疾参与者、固定步行速度）获得POS值。
