Biomechanical model-based multi-sensor motion estimation

Guanhong Tao, Zhipei Huang, Yingfei Sun, Shengyun Yao, Jiankang Wu
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引用次数: 8

Abstract

Motion estimation drift has been a challenge in inertial sensor motion capture research. This paper presents a novel biomechanical model-based multi-sensor motion estimation method working on a group of sensor units attached to a limb. In this method, biomechanical model provides constraints and defines relationships among sensors. The motion parameters of neighboring segments are estimated together by using unscented Kalman filter with those constraints and relationships. The performance of this method is benchmarked through the optical/inertial combined capture experiments. The experiment results show that our algorithm increases the accuracy of motion estimation.
基于生物力学模型的多传感器运动估计
运动估计漂移一直是惯性传感器运动捕捉研究中的一个难题。本文提出了一种基于生物力学模型的多传感器运动估计方法,该方法适用于一组附着在肢体上的传感器单元。在该方法中,生物力学模型提供约束并定义传感器之间的关系。结合约束条件和相互关系,利用无气味卡尔曼滤波对相邻段的运动参数进行估计。通过光学/惯性组合捕获实验对该方法的性能进行了基准测试。实验结果表明,该算法提高了运动估计的精度。
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