Okkalidis Nikiforos, Camilleri Kenneth P, Gatt Alfred, Bugeja Marvin K, Falzon Owen
Centre for Biomedical Cybernetics, University of Malta, Msida, Malta.
Department of Systems and Control Engineering, University of Malta, Msida, Malta.
Biomed Tech (Berl). 2020 Jun 25. doi: 10.1515/bmt-2019-0163.
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.
在过去几年中,人们对用于运动步态分析的足部惯性传感器及其他辅助传感器进行了广泛研究。尽管与采用光学运动捕捉系统获得的结果相比,这些传感器的精度仍然较低,但它们的小型化和低成本使得在实验室条件和现实生活场景中能够估算运动时空参数。这项工作的目的是通过系统的文献研究,全面介绍这一科学领域,将当前的先进方法分为三个主要部分:(1)零速度区间检测技术;(2)假设及传感器的使用;(3)足部姿态和轨迹估计方法。在PubMed、IEEE Xplore和谷歌学术数据库中搜索了1995年至2018年12月发表的文章。该研究集中在两个类别:(a)零速度区间检测方法;(b)足部姿态和轨迹估计方法。从检索到的文章中确定了所采用的假设以及传感器的潜在用途。提供了技术特性、分类方法、应用条件、优点和缺点,同时,在本综述中首次对假设和传感器的使用进行了识别、分类并呈现。在考虑诸如人在平坦地面行走等假设的受限实验室环境中,步态参数估计已经取得了相当大的进展。相反,依赖较少约束假设且因此适用于日常生活的方法,其结果准确性较低。基于规则的方法主要用于检测零速度区间,同时也提出了更复杂的技术,这可能会得出更准确的步态参数。综述过程表明,目前用于步态参数估计的性能最佳的方法是使用惯性传感器与辅助传感器(如超声波传感器、接近传感器和摄像头)相结合。然而,当使用单个惯性传感器时,实验评估方案要更加全面。最后,需要强调的是,通过在整个足部运动过程中而非仅像目前这样部分地收集测量数据,使用辅助传感器的设置的准确性可能会进一步提高。本综述确定了需要研发能够在无约束环境和各种步态特征下稳健估计运动步态参数的方法和设置。