Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan.
Sci Rep. 2022 Mar 30;12(1):5368. doi: 10.1038/s41598-022-09372-w.
State-of-the-art estimation methods using inertial measurement units (IMUs) for global continuous gait path and local stepwise gait trajectory during walking have been developed. However, estimation methods for continuous gait trajectory integrating both these aspects with high accuracy are almost lacking. Thus, continuous gait trajectory estimation using a single shank-worn IMU with high accuracy is proposed in this study. This method calculates three-dimensional local stepwise gait trajectory based on IMU measurement data extracted between adjacent middle points of stance phases during walking. Continuous gait trajectory is estimated by concatenating adjacent local stepwise gait trajectories based on relative angles determined according to stride vectors and shank orientations. Evaluation experiments results obtained using the optical motion capture system with 12 healthy participants demonstrated estimation errors in the stride length (- 0.027 (- 0.054 to - 0.006) m) and turning angle (0.7 (- 0.2-1.7)°), and normalized endpoint position error (0.029 (0.019-0.04) m). Comparing with previous reports, the proposed method integrally achieves a continuous gait trajectory with a low estimation error level in both local and global aspects despite the continuous measurement of multiple gait cycles. The proposed simple and low-cost method can be applied in the medical field and contribute to expansion of the application of precise gait information in daily life.
已经开发出了使用惯性测量单元(IMU)进行全球连续步态路径和局部分步步态轨迹的最新估计方法。然而,几乎缺乏将这两个方面结合起来并具有高精度的连续步态轨迹估计方法。因此,本研究提出了一种使用单腿穿戴式 IMU 进行高精度连续步态轨迹估计的方法。该方法基于行走过程中相邻支撑阶段中点之间的 IMU 测量数据计算三维局部分步步态轨迹。连续步态轨迹是根据步幅向量和小腿方向确定的相对角度,通过连接相邻的局部分步步态轨迹来估计的。使用具有 12 名健康参与者的光学运动捕捉系统进行的评估实验结果表明,步长(-0.027(-0.054 至-0.006)m)和转弯角度(0.7(-0.2-1.7)°)的估计误差,以及归一化端点位置误差(0.029(0.019-0.04)m)。与之前的报告相比,尽管连续测量多个步态周期,但该方法整体上在局部和全局方面都实现了低估计误差水平的连续步态轨迹。该简单且低成本的方法可应用于医学领域,并有助于扩展日常生活中精确步态信息的应用。