arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1606.03754eess.SYcs.ROcs.SY
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迈向自校准的惯性人体运动捕捉

Towards Self-Calibrating Inertial Body Motion Capture

University of Kaiserslautern(凯泽斯劳滕大学)

Bertram Taetz, Gabriele Bleser, Markus Miezal

AI总结:

本文提出了一种新的在线方法,用于同时估计人体运动的肢体方位和位置及传感器到肢体的校准参数,结合了先进的运动、测量和生物力学模型及新的随机方程和先验知识。

摘要:

本文提出了一种新的在线方法,用于同时估计人体运动的肢体方位和位置及传感器到肢体的校准参数,结合了先进的运动、测量和生物力学模型及新的随机方程和先验知识。这些基于多体系统的运动学、解剖和身体形状信息,以及用于正则化的参数属性。这导致了一个受约束的加权最小二乘问题,以滑动窗口的方式求解。磁力计信息目前仅用于初始化,而估计本身无需磁力计。该方法在模拟数据和真实数据上进行了测试,数据来自下肢配置。

英文摘要:

This paper presents a novel online capable method for simultaneous estimation of human motion in terms of segment orientations and positions along with sensor-to-segment calibration parameters from inertial sensors attached to the body. In order to solve this ill-posed estimation problem, state-of-the-art motion, measurement and biomechanical models are combined with new stochastic equations and priors. These are based on the kinematics of multi-body systems, anatomical and body shape information, as well as, parameter properties for regularisation. This leads to a constrained weighted least squares problem that is solved in a sliding window fashion. Magnetometer information is currently only used for initialisation, while the estimation itself works without magnetometers. The method was tested on simulated, as well as, on real data, captured from a lower body configuration.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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