惯性人体运动捕捉：从生物力学到近期的传感器融合方法再回归
Inertial Human Motion Capture: From Biomechanics to Recent Sensor Fusion Methods and Back
AI总结：
该综述聚焦惯性人体运动捕捉的运动学，介绍确定生物力学问题转化公式的关键方面，指出从IMU估计运动学的挑战及常用方法局限，分享近期有潜力的方法，并通过下肢关节角度估计用例说明相关问题及应用，旨在弥合两社区差距。
AI中文摘要：
惯性测量单元（IMU）是捕捉人体运动的一种有前景的方式，但从IMU测量中获取有意义的生物力学量并非易事。本教程式综述聚焦运动学，介绍四个关键方面（惯性人体运动捕捉目标、环境条件、主体及属性、运动特征），以确定如何将生物力学问题转化为用于融合惯性传感器测量的适当公式。我们识别出从IMU进行运动学估计的三个基本挑战：IMU不提供关节角度的直接信息、IMU不测自身方向、现实环境和动力学影响传感器可靠性。虽有广泛使用的方法克服这些挑战，但在实际应用中有严重局限。我们分享对近期提出方法的见解，如利用人体运动链约束，其有潜力克服这些局限。我们还提出与四个关键方面相关问题，并说明其在下肢关节角度估计用例的方法选择中的应用，为此分享开源代码并比较传统流程与三种替代方法。我们的目标是弥合为人体运动捕捉开发方法的传感器融合社区与需要准确、易用且可靠方法在实验室外研究人体运动的生物力学社区之间的差距。
英文摘要：
Inertial measurement units (IMUs) are a promising means to capture human motion, yet obtaining meaningful biomechanical quantities from IMU measurements remains non-trivial. This tutorial-style review focuses on kinematics and introduces four key aspects (inertial human motion capture objective, environmental conditions, subject & attributes, and motion characteristics) to determine how to translate biomechanical problems into adequate formulations for the fusion of inertial sensor measurements. We identify three fundamental challenges for kinematics estimation from IMUs: IMUs do not provide direct information about the joint angle, IMUs do not measure their own orientation, and real-world environments and dynamics compromise sensor reliability. Though there exist widely-used methods to overcome these challenges, they suffer from severe limitations in real-life applications, e.g., the need for sensor-to-segment calibration, and the fact that magnetic field disturbances degrade joint angle accuracy. The full potential for many use-cases hence remains untapped in terms of accuracy and reliability. We share insights into recently proposed methods, e.g. exploiting the human body's kinematic chain constraints, having the potential to overcome these limitations. We also present guiding questions related to the four key aspects and illustrate their use for navigating the methodological landscape for the use-case of lower-extremity joint angle estimation, for which we share open-access code and compare the traditional workflow with three alternatives. Our aim is to bridge the gap between the sensor fusion community developing methods for human motion capture and the biomechanics community in need of accurate, easy-to-use, and reliable methods to study human motion outside of the laboratory.
