Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4210-4213. doi: 10.1109/EMBC48229.2022.9871162.
When using wearable sensors for measurement and analysis of human performance, it is often necessary to integrate and synchronise data from separate sensor systems. This paper describes a synchronization technique between IMUs attached to the shanks and insoles attached at the feet and aims to solve the need to compute the ankle joint angle, which relies on synchronized sensor data. This will additionally enable concurrent analysis using gait kinematic and kinetic features. A proof-of-concept of the algorithm, which relies on cross-correlation of gyroscope sensor data from the shank and foot, to align the sensor systems is demonstrated. The algorithm output is validated against those signals synchronized using manually annotated heel-strike and toe-off ground-truth signal landmarks, identified in both the shank and feet signals using previously published definitions. Results demonstrate that the developed algorithm is capable of synchronizing both sensor systems, based on IMU data from both healthy participants and participants suffering from knee osteoarthritis, with a mean lag time bias of 25.56ms when compared to the ground truth. A proof-of-concept of technique to synchronise IMUs attached to the shanks and insoles attached at the feet is demonstrated and offers an alternative approach to sensor system synchronisation.
当使用可穿戴传感器来测量和分析人体表现时，通常需要整合和同步来自单独传感器系统的数据。本文描述了一种附着在小腿上的惯性测量单元（IMU）和附着在脚上的鞋垫之间的同步技术，旨在解决计算踝关节角度的需求，该角度依赖于同步的传感器数据。这还将能够同时使用运动学和动力学特征进行分析。本文展示了一种基于小腿和脚部的陀螺仪传感器数据互相关的算法概念验证，以对齐传感器系统。该算法的输出结果与使用手动注释的后跟撞击和脚趾离地地面真实信号标记进行同步的信号进行了验证，这些标记使用之前发布的定义在小腿和脚部信号中进行了识别。结果表明，该开发的算法能够基于来自健康参与者和膝骨关节炎参与者的 IMU 数据，同步附着在小腿上的传感器和附着在脚上的传感器系统，与地面真实情况相比，平均滞后时间偏差为 25.56ms。本文展示了一种附着在小腿上的惯性测量单元（IMU）和附着在脚上的鞋垫之间的同步技术的概念验证，为传感器系统同步提供了一种替代方法。
