Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Physiology, Nutrition and Biomechanics, The Swedish School of Sport and Health Sciences, Sweden.
J Biomech. 2024 Jan;162:111907. doi: 10.1016/j.jbiomech.2023.111907. Epub 2023 Dec 19.
Spatiotemporal gait parameters such as step time and walking speed can be used to quantify gait performance and determine physical function. Inertial measurement units (IMUs) allow for the measurement of spatiotemporal gait parameters in unconstrained environments but must be validated against a gold standard. While many IMU systems and algorithms have been validated during treadmill walking and overground walking in a straight line, fewer studies have validated algorithms during more complex walking conditions such as continuous turning in different directions. This study explored the concurrent validity in a population of healthy adults (range 26-52 years) of three different algorithms using lumbar and foot mounted IMUs to calculate spatiotemporal gait parameters: two methods utilizing an inverted pendulum model, and one method based on strapdown integration. IMU data was compared to a Vicon twelve-camera optoelectronic system, using data collected from 9 participants performing straight walking and continuous walking trials at different speeds, resulting in 162 walking trials in total. Intraclass correlation coefficients (ICC) for absolute agreement were calculated between the algorithm outputs and Vicon output. Temporal parameters were comparable in all methods and ranged from moderate to excellent, except double support time which was poor. Strapdown integration performed better for estimating spatial parameters than pendulum models during straight walking, but worse during turning. Selecting the most appropriate model should take into consideration both speed and walking condition.
时空步态参数，如步时和行走速度，可以用于量化步态表现和确定身体功能。惯性测量单元（IMU）允许在非约束环境中测量时空步态参数，但必须与黄金标准进行验证。虽然许多 IMU 系统和算法已经在跑步机行走和直线地面行走中得到了验证，但在更复杂的行走条件下，如不同方向的连续转弯，验证算法的研究较少。本研究在健康成年人（年龄范围 26-52 岁）中，使用腰部和脚部安装的 IMU 来计算时空步态参数，对三种不同算法的同时有效性进行了探索：两种利用倒立摆模型的方法，以及一种基于捷联惯导的方法。IMU 数据与 Vicon 十二相机光电系统进行了比较，使用 9 名参与者在不同速度下进行直线行走和连续行走试验的数据，总共进行了 162 次行走试验。算法输出与 Vicon 输出之间的绝对一致性的组内相关系数（ICC）进行了计算。所有方法的时间参数都具有可比性，介于中等至优秀之间，除了双支撑时间较差。在直线行走中，捷联惯导比摆模型更适合估计空间参数，但在转弯时表现较差。选择最合适的模型应同时考虑速度和行走条件。
