Sy Luke, Lovell Nigel H, Redmond Stephen J
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4858-4862. doi: 10.1109/EMBC44109.2020.9175684.
This paper presents an algorithm that makes novel use of distance measurements alongside a constrained Kalman filter to accurately estimate pelvis, thigh, and shank kinematics for both legs during walking and other body movements using only three wearable inertial measurement units (IMUs). The distance measurement formulation also assumes hinge knee joint and constant body segment length, helping produce estimates that are near or in the constraint space for better estimator stability. Simulated experiments have shown that inter-IMU distance measurement is indeed a promising new source of information to improve the pose estimation of inertial motion capture systems under a reduced sensor count configuration. Furthermore, experiments show that performance improved dramatically for dynamic movements even at high noise levels (e.g., σ = 0.2 m), and that acceptable performance for normal walking was achieved at σ = 0.1 m. Nevertheless, further validation is recommended using actual distance measurement sensors.
本文提出了一种算法，该算法创新性地将距离测量与约束卡尔曼滤波器结合使用，仅使用三个可穿戴惯性测量单元（IMU），就能在行走和其他身体运动过程中准确估计双腿的骨盆、大腿和小腿运动学。距离测量公式还假定膝关节为铰链关节且身体各节段长度恒定，有助于生成接近或处于约束空间内的估计值，从而提高估计器的稳定性。模拟实验表明，在传感器数量减少的配置下，IMU 间的距离测量确实是一种很有前景的新信息源，可用于改进惯性运动捕捉系统的姿态估计。此外，实验表明，即使在高噪声水平（例如，σ = 0.2 m）下，动态运动的性能也有显著提高，在 σ = 0.1 m 时可实现正常行走的可接受性能。尽管如此，建议使用实际距离测量传感器进行进一步验证。
