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The Lower Limbs Kinematics Analysis by Wearable Sensor Shoes

机译:可穿戴式传感器鞋的下肢运动学分析

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摘要

Inertial sensors designed for wearable devices in lower limbs need to be calibrated with complicated steps before measurements, and the attachments of body also influence the normal motion and bring measurement errors caused by relative movements. This paper proposes a method using ground reaction forces (GRFs) and moments measured by a pair of wearable sensor shoes to estimate the joint angles of lower limbs for gait analysis. The wearable sensor shoes are combined with motion sensors to construct a wearable gait analysis system in our previous research. Based on the reliable measurements of the wearable system, neural networks are trained with GRFs or GRFs and moments as an input, and the joint angles as the output. The joint angles obtained from the trained models and the measurements of the motion capture system show good agreements. The correlation coefficients () are more than 0.7, 0.9, and 0.9 for the joint angle of ankle, knee, and hip, respectively, and the normalized root mean square errors are less than 8°, 9°, and 5° for the three joints, respectively. The comparison results show low performance of rectification of biases and distortion of waveforms. As a major advantage, the method to acquire kinematics using the wearable sensor shoes is relative simple and reliable compared with multi-inertial sensors on body segments.
机译:为下肢可穿戴设备设计的惯性传感器在测量之前需要通过复杂的步骤进行校准,并且身体的附着也会影响正常运动并带来由相对运动引起的测量误差。本文提出了一种方法,该方法利用地面反作用力(GRF)和由一双可穿戴传感器鞋测量的力矩来估计下肢的关节角度以进行步态分析。在我们先前的研究中,可穿戴式传感器鞋与运动传感器相结合以构建可穿戴式步态分析系统。根据可穿戴系统的可靠测量结果,以GRF或GRF和力矩作为输入,并以关节角度作为输出来训练神经网络。从训练后的模型和运动捕捉系统的测量结果获得的关节角度显示出良好的一致性。踝,膝和髋关节角度的相关系数()分别大于0.7、0.9和0.9,并且三个方向的归一化均方根误差均小于8°,9°和5°关节。比较结果表明,偏置校正和波形失真的性能较低。作为主要优点,与身体部位上的多惯性传感器相比,使用可穿戴式传感器靴来获取运动学的方法相对简单且可靠。

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