Footscan步态分析系统在足迹检验中的应用初探

目的 探讨对人体行走运动主要步态参数的定量化分析技术.方法 以步态分析技术与足迹检验理论和方法为基础,运用先进的Footscan足底压力步态分析系统,对154名年龄在18至55岁之间的健康男性正常行走的时间、空间和力学等主要步态参数进行检测和统计分析.结果 Footscan系统采集的数据信息客观、全面,步态参数的量化分析方便、准确.Footscan系统能为足迹检验向定量化和自动化发展提供更加可靠的手段.     

一种基于动态比对表的步态特征检验方法

目的建立使用动态比对表检验步态等人像运动特征的具体方法,为人像同一性鉴定中的动态特征比对提供方法和依据。方法从视频采集、预处理、特征提取、识别判断等流程出发考察现有的步态自动识别方法,并依据人像鉴定的实际需求进行分析。结果动态比对表制作应当依照检材、样本采集、归一化处理和制作、标识与输出等步骤,并考虑步态周期、视频参数等制作细节。结论使用动态比对表对人像运动特征进行考察有助于特征的展示,能够成为鉴定人进行人像鉴定的有效辅助手段。     

青年人快速行走足底压力变化的研究

目的研究足底各区域压力分布的变化及其规律,为案件现场足迹分析提供理论基础与科学依据,促进足迹标准化与定量化检验。方法利用Footscan足底压力步态分析系统,采集15名青年人快速行走和常速行走时足底压力的数据,运用SPSS软件对两种步速下足底主要承重区域压力的分布特点作相关性分析和比较研究。结果足底各区域压力分布相关性不大,反映出压力有不同程度的改变。结论现场足迹分析时,应结合具体案情及犯罪嫌疑人的活动情况,准确把握足迹特征的变化以获得理想的分析结果。     

青年人正常行走左右足底压力分布关系的研究

目的研究左右足底压力分布及变化规律，为利用现场左右足迹分析年龄等人身特点提供科学依据，也为足迹定量化分析与检验奠定基础。方法采用Footscan足底压力步态分析系统，选择30名青年男性，对其正常行走时足底压力分布数据进行采集、处理，运用SPSS12．0统计软件对左右足底6个区域的平均压强进行Pearson相关分析。结果左右足相应区域足底压力分布具有不同程度的相关性。结论正常行走左右足跟区压强相关性最好，在利用现场左右足迹分析年龄等人身特点时，应以跟区特征为主，其它区域特征辅助。     

脚步声识别技术研究进展

得益于高性能计算机和深度学习算法的不断进步,生物特征识别技术得到快速发展。脚步声识别技术利用人行走时发出的声音或震动信号实现行走人的身份识别,具有隐蔽性、非接触式、不易被伪造和无需被识别人配合等优点,是一种非常有潜力的生物特征识别技术。本文概述了脚步声识别系统的组成部分、基本概念以及脚步声识别系统的性能评价指标,总结了脚步声识别技术中的信号采集方法及其使用的仪器设备、降噪和端点检测信号预处理关键技术、时频域和声学特征参数提取、各种模式识别方法在脚步声识别技术中的应用等方面的研究成果。最后,系统地分析了脚步声识别技术研究中尚未解决的问题,探讨了该研究领域需要进一步探索的热点问题和未来的发展趋势。     

生物特征识别技术

生物特征识别技术，是指利用计算机技术将人体所固有的生理特征或行为特征收集起来并进行处理，从而实现个人身份认证的技术。生物特征由人的生理特征和行为特征组成。人的生理特征与生俱来，如掌形、指纹、眼睛（视网膜和虹膜）、脸型、人体气味、皮肤毛孔、手腕／手的血管纹理和DNA等；行为特征则是后天习惯使然，如签名、语音、行走的步态、击打键盘的力度等。     

步长变化对踏痕影响的比较研究

本文通过大量的实验对不同步长下步态特征中的踏痕变化情况进行统计、分析,总结步长变化与踏痕间的内在联系和变化规律特点,为利用步幅特征进行足迹定量化检验鉴定提供理论依据。     

步长变化对足迹跟压痕迹特征影响的研究

本文通过大量的实验对不同步长下步态特征中的跟压痕迹的不同变化情况进行统计、分析,比较,归纳总结出步长变化与跟压痕特征之间的内在联系和变化规律特点,对传统的经验型检验、分析方法加以修正与补充,进而为利用步法特征进行定量化检验鉴定提供理论依据。     

立体足迹计算机自动识别系统的应用

足迹是犯罪分子在作案过程中遗留在承受客体上并能反映赤足或穿鞋袜的结构形态和人体行走动力的痕迹。在我国利用步法追踪和足迹检验认定人身起步于50年代,当时还是处于少数人掌握的经验阶段。上世纪80年代以后,在马玉林先生的步法追踪的基础上,科技工作者沿着形象特征和步法特征两条线索,使足迹检验的科学理论有了较大发展。在足迹认定人身方面,形成了诸多各有所长的检验方法。为了实现足迹检验现代化,笔者和有关部门共同研制了“立体足迹计算机自动检验系统(以下简称系统),介绍如下。1“系统”性能与功能1.1原理人体站立或行走条件下,脚掌…     

步态识别技术的工作原理及发展趋势

步态识别技术是一种新兴的生物识别技术,旨在通过人走路的姿态进行身份识别。步态是指人在行走时的一种复杂的行为特征。罪犯或许采取多种方式掩饰自己,甚至不让自己身上哪怕一根毛发掉到犯罪现场,但是有一样东西他们是很难控制的,这就是走路的姿势。根据研究显示,人人都有截然不同的走路姿势,人的肌肉的力量、肌腱和骨骼的长度和密度、肢体的协调能力、重心、体重、生理条件以及各人走路的风格都存在着细微的差别。对     

The accuracy and validity of the Sheffield Features of Gait Tool

Gait is now widely used in the UK as a contributor to identification, and increasing interest is being shown in its use in both Europe and the US. One of the long standing criticisms of the use of gait as evidence has been the lack of a validated standard methodology. With the publication of the ‘Code of practice for forensic gait analysis’, and the adoption of the code as part of the ‘Codes of Practice and Conduct for forensic science providers and practitioners in the Criminal Justice System’ by the Forensic Science Regulator, forensic gait analysts are now required to provide evidence of the testing of the methods used. The Sheffield Features of Gait Tool is specifically designed to assist observational gait analysis in the forensic context, and was developed by forensic gait analysis practitioners based on their casework and trial experience. Birch et al 2019 reported the findings of a study undertaken to assess the repeatability and reproducibility of the tool. This paper reports the findings of a study undertaken to assess the accuracy with which analysts identified features of gait when using the 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 results showed a mean accuracy score of 134.92 out of a possible 180 (74.96%), a standard deviation of 9.49 (5.27%) and a coefficient of variation of 7.03%, demonstrating a good degree of consistency between the scores (Cronbach’s alpha <0.90; ANOVA p-value <0.05).The findings of this study, coupled with those of the Birch et al 2019 study which showed there to be good levels of both repeatability and reproducibility of observations of features of gait made by the participants, suggest that the Sheffield Features of Gait Tool is a valid and fit for purpose method of observing and recording features of gait in the forensic context. The use of the tool provides the basis of a standardised methodology for observational gait analysis in the forensic context.     

Gait analysis in forensic medicine*

Recordings from video surveillance systems are used as evidence from crime scenes. It would be useful to perform comparisons between disguised perpetrators and suspects based on their gait. We applied functional anatomical and biomechanical knowledge to analyze the gait of perpetrators, as recorded on surveillance video. Using a structured checklist, which addresses the single body segments during gait, we were able to give a statement concerning the gait patterns. Characteristic parameters were, e.g., varus instability in the knee at heel strike, and larger lateral flexion of the spinal column to one side than the other. Based on these characteristic features, we are able to state with reasonable certainty whether the suspect could be the perpetrator, but it is not possible to identify the perpetrator positively. Nevertheless, we have been involved in several cases where the court has found that this type of gait analysis was a valuable tool.     

Reliable Gait Recognition Using 3D Reconstructions and Random Forests – An Anthropometric Approach

Photogrammetric measurements of bodily dimensions and analysis of gait patterns in CCTV are important tools in forensic investigations but accurate extraction of the measurements are challenging. This study tested whether manual annotation of the joint centers on 3D reconstructions could provide reliable recognition. Sixteen participants performed normal walking where 3D reconstructions were obtained continually. Segment lengths and kinematics from the extremities were manually extracted by eight expert observers. The results showed that all the participants were recognized, assuming the same expert annotated the data. Recognition based on data annotated by different experts was less reliable achieving 72.6% correct recognitions as some parameters were heavily affected by interobserver variability. This study verified that 3D reconstructions are feasible for forensic gait analysis as an improved alternative to conventional CCTV. However, further studies are needed to account for the use of different clothing, field conditions, etc.     

The repeatability and reproducibility of the Sheffield Features of Gait Tool

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.     

The identification of individuals by observational gait analysis using closed circuit television footage: Comparing the ability and confidence of experienced and non-experienced analysts

Gait is known to have been used as evidence since 1839, initially based on the apocryphal belief that a person can be identified by their gait. The potential uniqueness of gait has yet to be proven, and therefore gait is currently considered to be a contributor to identification rather than a method of identification. In 2013 Birch et al. [1] published the findings of an investigation into the ability of individuals with experience in gait analysis to identify people by observing features of gait recorded by closed circuit television cameras. The study showed that the participants made correct decisions in 71% of cases, significantly better than would have been expected to have occurred by chance. However, the presentation of gait evidence is not limited to witnesses with experience in gait analysis. This study compared the abilities and confidence of participants with experience in gait analysis with those of participants with no experience of gait analysis using the methodology of Birch et al. 2013 [1]. The results showed no statistically significant difference in the number of correct identification decisions made by the two groups of participants, although the participants with experience of gait analysis made slightly more false negative than false positive decisions, whereas the participants with no experience made more false positive than false negative decisions. The participants with no experience in gait analysis reported significantly more confidence in their decisions than did the participants with experience (p < 0.05). The results suggest that lay people giving gait based evidence are likely to be more confident in their assertions as to identity based on that evidence, than would a witness with experience of gait analysis. Careful consideration therefore needs to be given to the submission of gait based evidence by lay witnesses.     

Capture – A behavioral analysis tool for applications and documents

In this paper, we present Capture, a tool for behavioral analysis of applications for the Win32 operating system family. Capture is able to monitor the state of a system during the execution of applications and processing of documents, which provides the analyst with insights on how the software operates even if no source code is available. Capture differs from existing behavioral analysis tools in its ability to monitor state changes on a low kernel level and its ability to be easily used across operating systems, various versions and configurations. Capture provides a powerful mechanism to exclude event noise that naturally occurs on an idle system or when using a specific application. This mechanism is fine-grained and allows an analyst to take into account the process that causes the various state changes. As a result, this mechanism even allows Capture to analyze the behavior of documents that execute within the context of an application. We demonstrate Capture's capabilities by analyzing a malicious Microsoft Word document.     

Evaluation of the Differex™ System

Differential extraction is an efficient method to separate sperm cells from epithelial cells. A manual Chelex^®-100 based method is used at the Swedish National Laboratory of Forensic Science, SKL. The Differex™ System (Promega) uses a Proteinase K digestion of epithelial cells followed by centrifugation and phase separation. The sperm- and epithelial fractions are further purified with DNA IQ™ System (Promega) or with phenol/chloroform. The Differex™ System in combination with DNA IQ™ System were evaluated and compared to the Chelex^®-100 method used routinely. After modifications, the Differex™ System gave comparable results to the Chelex^®-100 method. The modifications included additional Proteinase K and DTT, longer incubation time and additional steps when removing the solid support from the Digestion Solution. In the Chelex^®-100 based method microscopic examination is done on the sperm pellet in a total volume of 50 μl. It was not possible to do a microscopic examination in less than 100 μl using the Differex™ System. Additionally the sperms were in clusters of epithelial cell debris. Microscopic examination is an important part of the differential extraction at SKL. Therefore, the Differex™ System will not be implemented at our laboratory.     

激光显微切割技术与低体系扩增方法的法医学应用探索

目的建立减少DNA低体积扩增过程中产生气泡的方法。方法采用激光显微切割技术、PALM系统收集目标细胞,并在1μL～1.5μL低体积扩增样本,加入扩增液0.7μL～0.8μL。结果低体积扩增反应中的失败位点比例为2.3%,比常规反应位点时常会产生气泡导致该位点样本扩增失败且达30%以上的比例显著降低。结论低体积扩增方法可以减少或克服扩增过程中气泡的产生,提高扩增成功率。     

磁珠法对混合斑中精子DNA批量自动提取的应用研究

目的 建立混合斑中精子DNA批量自动提取的方法.方法 对56例混合斑经第一步消化后的精子沉淀用DNA IQ试剂盒和Biomek（R）2000全自动核酸工作站自动提取纯化,3130xl型遗传分析仪分析结果.结果 56例混合斑检材均得到较好的STR分型结果.结论 DNA IQ试剂盒和Biomek（R）2000全自动核酸工作站批量自动提取纯化混合斑的方法快速简便,可应用于法庭科学实践.     

Patella sex determination by 3D statistical shape models and nonlinear classifiers

Sex determination is one of the essential steps in personal identification of an individual from skeletal remains. Most elements of the skeleton have been subjected to discriminant function analysis for sex estimation, but little work has been done in terms of the patella. This paper proposes a new sex determination method from the patella using a novel automated feature extraction technique. A dataset of 228 patellae (95 females and 133 males) was amassed from the William M. Bass Donated Skeletal Collection from the University of Tennessee and was subjected to noninvasive high resolution computed tomography (CT). After the CT data were segmented, a set of features was automatically extracted, normalized, and ranked. The segmentation process with surface smoothing minimizes the noise from enthesophytes and ultimately allows our methods to distinguish variations in patellar morphology. These features include geometric features, moments, principal axes, and principal components. A feature vector of dimension 45 for each subject was then constructed. A set of statistical and supervised neural network classification methods were used to classify the sex of the patellar feature vectors. Nonlinear classifiers such as neural networks have been used in previous research to analyze several medical diagnosis problems, including quantitative tissue characterization and automated chromosome classification. In this paper, different classification methods were compared. Classification success ranged from 83.77% average classification rate using labels from a Fuzzy C-Means (FCM) clustering step, to 90.3% for linear discriminant classification (LDC). We obtained results of 96.02% and 93.51% training and testing classification rates, respectively, using feed-forward backpropagation neural networks (NN). These promising results using newly developed features and the application of nonlinear classifiers encourage the usage of these methods in forensic anthropology for identifying the sex of an individual from incomplete skeletons retaining at least one patella.