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Inertial Sensors in Estimating Spatio-Temporal Parameters of Walking: Performance Evaluation and Error Analysis.

机译:估计步行时空参数的惯性传感器:性能评估和误差分析。

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摘要

The portability, ease of use and improved accuracy of miniature inertial sensors brought by current microelectromechanical system (MEMS) technology has inspired researchers to develop human movement monitoring system with body-fixed sensors. Although a large number of studies have attempted to explore the use of miniature inertial sensors in estimating walking speed for the past two decades, there still remain some questions regarding applying inertial sensors in estimating walking speed under different walking conditions and for different subject populations. In this thesis, I focus on evaluating and improving the performance of a shank-mounted mounted inertial measurement unit (IMU) based walking speed estimation method. My research can be divided into four parts. The first part was a systematic review regarding the state of the art of current development of the inertial sensor based walking speed estimation method. A total of 16 articles were fully reviewed in terms of sensor specification, sensor attachment location, experimental design and spatial parameter estimation algorithm. In the second part, a comprehensive performance evaluation was conducted, which included the treadmill and overground walking experiments with constraint on the walking speed, stride length and stride frequency. A systematic error was observed in the error analysis of this study, which was adjusted by subtracting the bias by linear regression. In the third part, a post-stroke subject overground walking experiment was carried out with an improved walking speed estimation method that reduced the systematic error caused by previous false initial speed assumption. In addition to walking speed estimation, the gait asymmetry for post-stroke hemiparetic gait was also evaluated with the proposed method. The last part was the sensor error model analysis. We elaborately analyzed and discussed the estimation errors involved in this method in order to completely understand the sensor error compensation in walking speed estimation algorithm design. Two existing sensor error models and one newly developed sensor error model were compared with the treadmill walking experiment, which demonstrated the effect of each sensor error component on the estimation result and the importance of the sensor error model selection.
机译:当前的微机电系统(MEMS)技术带来的微型惯性传感器的便携性,易用性和更高的精度,已激发研究人员开发具有人体固定传感器的人体运动监测系统。尽管在过去的二十年中,尽管有大量研究尝试探索使用微型惯性传感器估算步行速度,但仍存在一些有关在不同步行条件下和针对不同受试者人群中应用惯性传感器估算步行速度的问题。在本文中,我着重于评估和改进基于柄安装的惯性测量单元(IMU)的步行速度估计方法的性能。我的研究可以分为四个部分。第一部分是有关基于惯性传感器的步行速度估计方法的最新发展现状的系统综述。在传感器规格,传感器附件位置,实验设计和空间参数估计算法方面,共对16篇文章进行了全面审查。在第二部分中,进行了综合性能评估,其中包括跑步机和地面步行实验,这些实验对步行速度,步幅和步幅有约束。在这项研究的误差分析中观察到系统误差,可以通过线性回归减去偏差来进行调整。在第三部分中,使用改进的步行速度估计方法进行了中风后受试者的地面步行实验,该方法减少了先前错误的初始速度假设所引起的系统误差。除了估计步行速度外,还使用提出的方法评估了卒中后半步态的步态不对称性。最后一部分是传感器误差模型分析。为了全面理解步行速度估计算法设计中的传感器误差补偿,我们对这种方法涉及的估计误差进行了详尽的分析和讨论。将两个现有的传感器误差模型和一个新开发的传感器误差模型与跑步机步行实验进行了比较,证明了每个传感器误差分量对估计结果的影响以及传感器误差模型选择的重要性。

著录项

  • 作者

    Yang, Shuozhi.;

  • 作者单位

    Queen's University (Canada).;

  • 授予单位 Queen's University (Canada).;
  • 学科 Engineering Biomedical.;Biophysics Biomechanics.
  • 学位 M.A.Sc.
  • 年度 2011
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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