Birch Ivan, Birch Maria, Rutler Lucy, Brown Sarah, Burgos Libertad Rodriguez, Otten Bert, Wiedemeijer Mickey
Sheffield Teaching Hospitals NHS Foundation Trust, Woodhouse Clinic, 3 Skelton Lane, Sheffield S13 7LY, United Kingdom.
School of Health Sciences, University of Brighton, 49 Darley Road, Eastbourne BN20 7UR, United Kingdom.
Sci Justice. 2019 Sep;59(5):544-551. doi: 10.1016/j.scijus.2019.04.001. Epub 2019 Apr 19.
Gait, the pattern or style in which locomotion is undertaken, has kinematic characteristics that may occur in varying proportions of a population and therefore have discriminatory potential. Forensic gait analysis is the analysis, comparison and evaluation of features of gait to assist the investigation of crime. While there have been recent developments in automated gait recognition systems, gait analysis presented in criminal court to assist in identification currently relies on observational analysis by expert witnesses. Observational gait analysis has been the focus of considerable research, and it has been shown that the adoption of a systematic approach to both the observation and recording of features of gait improves the reliability of the analysis. The Sheffield Features of Gait Tool was developed by forensic gait analysis practitioners based on their casework and trial experience, and consists of more than a hundred features of gait and variances. This paper reports the findings of a study undertaken to assess the repeatability and reproducibility of the Sheffield Features of Gait Tool. Fourteen participants, with experience in observational gait analysis, viewed footage of computer generated avatars walking, and completed the features of gait tool on multiple occasions. The repeatability scores varied between participants from a highest score of 42.59 out of a maximum possible score of 45 (94.65%), to a lowest score of 30.76 (68.35%), with a mean score of 35.79 (79.54%) and a standard deviation of 3.59 (7.98%). The reproducibility scores for the assessment of each avatar varied from a highest score of 137.73 out of the best possible score of 180 (76.52%), to a lowest score of 127.21 (70.67%), with a mean score of 132.21 (73.45) and a standard deviation of 3.82 (2.12%). The results demonstrated that the use of the Sheffield Features of Gait Tool by experienced analysists resulted in what could be considered to be good levels of both repeatability and reproducibility. Some variation was shown to occur both between the results produced by different analysts, and between those produced from the analysis of different avatars. An understanding of the probative value of gait analysis evidence is an important facet of its submission as evidence, and the design and testing of standardized methods of analysis and comparison are an essential element of developing that understanding. This study is the first to test a purpose designed features of gait tool for use in forensic gait analysis.
步态,即进行移动的模式或方式,具有运动学特征,这些特征可能在不同比例的人群中出现,因此具有鉴别潜力。法医步态分析是对步态特征进行分析、比较和评估,以协助犯罪调查。虽然自动步态识别系统最近有了发展,但目前在刑事法庭上用于协助身份识别的步态分析仍依赖专家证人的观察分析。观察性步态分析一直是大量研究的重点,并且已经表明,采用系统的方法来观察和记录步态特征可以提高分析的可靠性。谢菲尔德步态特征工具是由法医步态分析从业者根据他们的办案和庭审经验开发的,包含一百多个步态特征和变量。本文报告了一项旨在评估谢菲尔德步态特征工具的可重复性和再现性的研究结果。十四名具有观察性步态分析经验的参与者观看了计算机生成的虚拟人行走的视频,并多次完成步态特征工具的填写。参与者的可重复性得分各不相同,最高得分为满分45分中的42.59分(94.65%),最低得分为30.76分(68.35%),平均得分为35.79分(79.54%),标准差为3.59分(7.98%)。对每个虚拟人的评估的再现性得分从满分180分中的137.73分(76.52%)到最低分127.21分(70.67%)不等,平均得分为132.21分(73.45),标准差为3.82分(2.12%)。结果表明,经验丰富的分析人员使用谢菲尔德步态特征工具可获得可认为是良好水平的可重复性和再现性。不同分析人员得出的结果之间以及对不同虚拟人进行分析得出的结果之间均显示出一些差异。理解步态分析证据的证明价值是将其作为证据提交的一个重要方面,而设计和测试标准化的分析和比较方法是形成这种理解的一个基本要素。本研究是首次测试专门设计用于法医步态分析的步态特征工具。