Ruder Matthew C, Hunt Michael A, Charlton Jesse M, Tse Calvin T F, Kobsar Dylan
Department of Kinesiology, McMaster University, Hamilton, ON, Canada.
Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada; Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada.
J Biomech. 2022 Sep;142:111263. doi: 10.1016/j.jbiomech.2022.111263. Epub 2022 Aug 18.
To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.
比较研究者放置和自我放置传感器的不同测量时段放置可靠性，并评估根据先前描述的对齐校正算法，研究者放置和自我放置传感器的波形及离散变量的有效性和可靠性。14名健康、无疼痛的参与者在两个数据收集时段接受了步态分析。在完成10次地面行走试验之前，参与者在其左胫骨上自行放置一个惯性传感器，研究者在其右胫骨上放置一个。基于主成分分析的算法进行轴校正后，通过欧几里得距离、波形和离散结果，评估每种传感器放置类型在日内和日间的有效性和可靠性。与自我放置的传感器（ICC = 0.55）相比，研究者放置的传感器的放置位置表现出良好的不同测量时段可靠性（ICC = 0.85）。同样，研究者放置的传感器的波形在所有变量上均表现出优异的有效性（CMC≥0.90），而自我放置的传感器在大多数轴上具有较高的有效性，但在内外侧加速度和额面角速度方面有效性有所降低。两种传感器放置类型的离散结果均表现出良好至优异的可靠性。一种简单的惯性传感器步态数据对齐校正算法在自我放置的传感器中显示出良好至优异的有效性和可靠性，无需额外的数据或测量。尽管在直线、水平行走过程中存在传感器放置误差，但该方法可用于轻松有效地对齐传感器，以改善自我放置传感器收集的数据中的三维步态数据结果。
