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A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis.
Biomedical Engineering / Biomedizinische Technik ( IF 1.7 ) Pub Date : 2020-06-25 , DOI: 10.1515/bmt-2019-0163
Nikiforos Okkalidis 1 , Kenneth P Camilleri 1, 2 , Alfred Gatt 3 , Marvin K Bugeja 2 , Owen Falzon 1
Affiliation  

The use of foot mounted inertial and other auxiliary sensors for kinematic gait analysis has been extensively investigated during the last years. Although, these sensors still yield less accurate results than those obtained employing optical motion capture systems, the miniaturization and their low cost have allowed the estimation of kinematic spatiotemporal parameters in laboratory conditions and real life scenarios. The aim of this work was to present a comprehensive approach of this scientific area through a systematic literature research, breaking down the state-of-the-art methods into three main parts: (1) zero velocity interval detection techniques; (2) assumptions and sensors’ utilization; (3) foot pose and trajectory estimation methods. Published articles from 1995 until December of 2018 were searched in the PubMed, IEEE Xplore and Google Scholar databases. The research was focused on two categories: (a) zero velocity interval detection methods; and (b) foot pose and trajectory estimation methods. The employed assumptions and the potential use of the sensors have been identified from the retrieved articles. Technical characteristics, categorized methodologies, application conditions, advantages and disadvantages have been provided, while, for the first time, assumptions and sensors’ utilization have been identified, categorized and are presented in this review. Considerable progress has been achieved in gait parameters estimation on constrained laboratory environments taking into account assumptions such as a person walking on a flat floor. On the contrary, methods that rely on less constraining assumptions, and are thus applicable in daily life, led to less accurate results. Rule based methods have been mainly used for the detection of the zero velocity intervals, while more complex techniques have been proposed, which may lead to more accurate gait parameters. The review process has shown that presently the best-performing methods for gait parameter estimation make use of inertial sensors combined with auxiliary sensors such as ultrasonic sensors, proximity sensors and cameras. However, the experimental evaluation protocol was much more thorough, when single inertial sensors were used. Finally, it has been highlighted that the accuracy of setups using auxiliary sensors may further be improved by collecting measurements during the whole foot movement and not only partially as is currently the practice. This review has identified the need for research and development of methods and setups that allow for the robust estimation of kinematic gait parameters in unconstrained environments and under various gait profiles.

中文翻译:

使用惯性和辅助传感器进行运动步态分析的脚部姿势和轨迹估计方法的综述。

在过去的几年中,已经广泛研究了将脚安装的惯性传感器和其他辅助传感器用于运动步态分析。尽管这些传感器仍然比使用光学运动捕获系统获得的结果准确度低,但是它们的小型化和低成本使得可以在实验室条件和现实生活中估计运动时空参数。这项工作的目的是通过系统的文献研究来介绍这一科学领域的综合方法,将最新的方法分为三个主要部分:(1)零速度间隔检测技术;(2)假设和传感器的利用;(3)脚的姿势和轨迹估计方法。在PubMed中搜索了1995年至2018年12月发表的文章,IEEE Xplore和Google Scholar数据库。研究集中在两类上:(a)零速度间隔检测方法;(b)脚的姿势和轨迹估计方法。已经从检索到的物品中识别出所采用的假设和传感器的潜在用途。本文提供了技术特性,分类方法,应用条件,优缺点,同时首次对假设和传感器的利用率进行了识别,分类和介绍。考虑到诸如人在平坦地板上行走等假设,在受限实验室环境中的步态参数估计方面已经取得了很大进展。相反,依赖较少约束假设的方法因此适用于日常生活,导致结果准确性降低。基于规则的方法主要用于零速度间隔的检测,而提出了更复杂的技术,这可能导致步态参数更准确。审查过程表明,目前最佳的步态参数估计方法是将惯性传感器与辅助传感器(如超声传感器,接近传感器和照相机)组合使用。但是,当使用单个惯性传感器时,实验评估协议要彻底得多。最后,我们着重指出,通过在整个足部运动过程中收集测量数据,不仅可以像目前的做法那样,还可以进一步收集使用辅助传感器的设置精度。基于规则的方法主要用于零速度间隔的检测,而提出了更复杂的技术,这可能导致步态参数更准确。审查过程表明,目前最佳的步态参数估计方法是将惯性传感器与辅助传感器(如超声传感器,接近传感器和照相机)组合使用。但是,当使用单个惯性传感器时,实验评估协议要彻底得多。最后,我们着重指出,通过在整个脚运动过程中收集测量数据,不仅可以像目前的做法那样,还可以进一步收集使用辅助传感器的设置精度。基于规则的方法主要用于零速度间隔的检测,而提出了更复杂的技术,这可能导致步态参数更准确。审查过程表明,目前最佳的步态参数估计方法是将惯性传感器与辅助传感器(如超声传感器,接近传感器和照相机)组合使用。但是,当使用单个惯性传感器时,实验评估协议要彻底得多。最后,我们着重指出,通过在整个足部运动过程中收集测量数据,不仅可以像目前的做法那样,还可以进一步收集使用辅助传感器的设置精度。审查过程表明,目前最佳的步态参数估计方法是将惯性传感器与辅助传感器(如超声传感器,接近传感器和照相机)组合使用。但是,当使用单个惯性传感器时,实验评估协议要彻底得多。最后,我们着重指出,通过在整个脚运动过程中收集测量值,不仅可以像目前的做法那样,还可以通过收集测量值来进一步提高使用辅助传感器的设置精度 审查过程表明,目前最佳的步态参数估计方法是将惯性传感器与辅助传感器(如超声传感器,接近传感器和照相机)组合使用。但是,当使用单个惯性传感器时,实验评估协议要彻底得多。最后,我们着重指出,通过在整个脚运动过程中收集测量值,不仅可以像目前的做法那样,还可以通过收集测量值来进一步提高使用辅助传感器的设置精度 当使用单个惯性传感器时。最后,我们着重指出,通过在整个脚运动过程中收集测量值,不仅可以像目前的做法那样,还可以通过收集测量值来进一步提高使用辅助传感器的设置精度 当使用单个惯性传感器时。最后,我们着重指出,通过在整个脚运动过程中收集测量值,不仅可以像目前的做法那样,还可以通过收集测量值来进一步提高使用辅助传感器的设置精度这项审查确定了研究和开发方法和设置的需要,这些方法和设置允许在不受约束的环境中和各种步态剖面下可靠地估计运动步态参数。
更新日期:2020-06-25
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