磁感测关节角度的可行性研究

Fereshteh Shahmiri, sshahmiri
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引用次数: 1

摘要

惯性测量单元(imu)广泛用于身体运动跟踪应用。尽管它们无处不在,但随着时间的推移,它们经常受到传感器漂移和环境干扰的影响。此外,它们的用例主要局限于具有缓慢变化的加速度和低动态运动的应用程序。传感器融合算法用于遇到更动态、更快运动的场景。然而,这样的算法通常会带来很高的计算成本。在这项工作中,我们提出了一种低漂移,计算效率高的运动跟踪系统,该系统可以抑制环境磁噪声,并适用于各种运动动力学。我们用定位磁铁增强了惯性传感器,并实现了一种定位算法,该算法接受磁场测量,并在传感器在磁铁附近移动时输出传感器位置。对于围绕中心关节运动的应用,我们将位置跟踪扩展到关节角度测量平台。我们进行了两项初步研究来评估我们的系统性能,并通过计算机视觉系统验证了我们的系统。我们的第一项研究使用了一个角度测量装置来评估角度估计的漂移减少。我们的方法与常用的基于imu的方法进行了比较。我们从4次学习中收集了60分钟的数据,包括静态条件和各种动态运动。运动的角速度范围为0到47(°/秒)。结果表明,静态运动的均方根误差(RMSE)为1°,动态运动的均方根误差为2.7°。在第二项研究中,一个在身体上的装置监测由飞行员用户进行的膝关节屈曲和伸展。我们从4次学习中收集了30分钟的数据。我们的系统报告平均RMSE为3.7°的动态运动,平均角速度为17(°/秒)。基于这些有希望的结果,在未来的工作中,我们将把我们的用户研究扩展到更多的用户,以评估泛化性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Angle Measurements Using Magnetic Sensing: A Feasibility Study
Inertial measurement units (IMUs) are extensively used for body motion tracking applications. Despite their ubiquity, they often suffer from sensor drift over time, and environmental disturbances. Additionally, their use cases are mostly limited to applications with slowly varying accelerations and low-dynamic motions. Sensor fusion algorithms are used for scenarios where more dynamic, faster motions are encountered. However, such algorithms often come with high computational costs. In this work, we present a low-drift, computationally-efficient motion tracking system that suppresses ambient magnetic noise and is applicable to various motion dynamics. We augmented inertial sensors with localized magnets, and implemented a localization algorithm that takes in the magnetic measurements and outputs the sensor positions as the sensors move in the vicinity of the magnets. For applications with movements around a central joint, we extended our position tracking to a joint angle measurement platform. We conducted two preliminary studies to evaluate our system performance, and validated our system against a computer vision system. Our first study uses a goniometric setup to evaluate drift-reductions in angle estimates. Our method is compared against a commonly-used IMU-based method. We collected 60 minutes of data from 4 study sessions, with both static conditions and various dynamic motions. The motions had angular velocities ranging from 0 to 47 (°/sec). Results show the average root mean square error (RMSE) of 1° for static and 2.7° for dynamic motions. In the second study, an on-body setup monitors the knee flexions and extensions performed by a pilot user. We collected 30 minutes of data from 4 study sessions. Our system reports the average RMSE of 3.7° for dynamic motions with an average angular velocity of 17 (°/sec). Based on these promising results, in future work we will extend our user studies to a greater number of users to evaluate the generalizability.
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