Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstrasse 20, 52074 Aachen, Germany.
Sensors (Basel). 2017 Mar 29;17(4):713. doi: 10.3390/s17040713.
We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals. The model incorporates a two-dimensional kinematic description in the sagittal plane for the calculation of muscle lever arms and torques. To reduce estimation errors due to model uncertainties, a filtering algorithm is necessary that employs segmental orientation sensor measurements. Because of the model's inherent nonlinearities and nonsmooth dynamics, a square-root cubature Kalman filter was developed. The performance of the novel estimation approach was evaluated in silico and in an experimental procedure. The experimental study was conducted with body-worn sensors and a test-bench that was specifically designed to obtain reference angle and torque measurements for a single joint. Results show that the filter is able to reconstruct joint angle positions, velocities and torque, as well as, joint stiffness during experimental test bench movements.
我们致力于使用可穿戴测量技术来估计生物力学参数。特别是,我们专注于对背屈/跖屈矢状面踝关节刚度的估计。为此,我们提出了一种新的基于肌电信号的小腿非线性生物力学模型。该模型在矢状面采用二维运动学描述来计算肌肉力臂和扭矩。为了减少由于模型不确定性导致的估计误差,需要采用分段方向传感器测量的滤波算法。由于模型的固有非线性和非光滑动力学,我们开发了一种平方根容积卡尔曼滤波器。新型估计方法的性能在仿真和实验过程中进行了评估。实验研究使用了可穿戴传感器和专门设计的测试台进行,以获得单个关节的参考角度和扭矩测量值。结果表明,该滤波器能够在实验测试台运动过程中重建关节角度位置、速度和扭矩,以及关节刚度。