ForceID Dataset A

This version (Version 6) is the dataset associated with the article, Duncanson, K.A.; Thwaites, S.; Booth, D.; Hanly, G.; Robertson, W.S.P.; Abbasnejad, E.; Thewlis, D. Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data. Sensors 2023, 23, 3392. https://doi.org/10.3390/s23073392. Please see Version 3 for the dataset and description relevant to the article, Duncanson, Kayne; Thwaites, Simon; Booth, David; Abbasnejad, Ehsan; Robertson, William; Thewlis, Dominic (2021): The Most Discriminant Components of Force Platform Data for Gait Based Person Re-identification. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.16683229.v1.Dataset overviewThis dataset was acquired for research on the use of gait as a (soft) biometric for person re-identification (re-ID)/recognition; however, it may be used to answer a variety of research questions. It is one of the largest and most complex force platform datasets purpose built for person re-ID. It contains 5327 walking trials from 184 healthy participants, with inter- and intra-individual variation in clothing, footwear, and walking speed, as well as inter-individual variation in time between trials (data was collected over two sessions separated by 3-14 days depending on the individual). The dataset was generated through a repeated measures experiment (approved by the Human Research Ethics Committee - approval No. H-2018-009) conducted at The University of Adelaide gait analysis laboratory.Experimental protocolAt the start of each session, age, sex, mass, height, and footwear type were recorded, as participants wore personal clothing and footwear. Footwear was also photographed for future reference. Next, participants walked in one direction along the length of the laboratory (≈10m) five times at three self-selected speeds: preferred, slower than preferred, and faster than preferred. Two in-ground OPT400600-HP force platforms (Advanced Mechanical Technology Inc., USA) in the center of the laboratory measured GRFs and GRMs during left and right footsteps. These measures, along with calculated COP coordinates, were acquired through Vicon Nexus (Vicon Motion Systems Ltd, UK) at 2000 Hz. Of note, all trials in this dataset were complete foot contacts; that is, each foot contacted completely within the area of each force platform, as identified from video footage.User guideThe code repository to implement this dataset can be found at GitHub - kayneduncanson1/ForceID-Study-1: Repository for the article, 'Deep Metric Learning for Scalable Gait Based Person Re-identification Using Force Platform Data'. The dataset is organised into separate spreadsheets that each contain all samples of a particular component from a given force platform (named as component_platform). Both raw and processed ('pro') versions are available. Within each of the data spreadsheets is accessory information about each trial in the first five columns, followed by the data in column six onward. Within the metadata spreadsheet are the ID numbers and their associated demographics. The ID numbers in the first column are from the private version of the dataset that was implemented for the manuscript. These can be cross-referenced with the ID numbers generated for the public dataset in the code repository. This means that the challenging IDs listed in Supplemental Material - Table VII (in the article) can be located in this dataset.

本版本（第6版）为与文章《Duncanson, K.A.; Thwaites, S.; Booth, D.; Hanly, G.; Robertson, W.S.P.; Abbasnejad, E.; Thewlis, D. 深度度量学习在基于力平台数据的可扩展步态人物重识别中的应用》相关的数据集。请参阅第3版以获取与文章《Duncanson, Kayne; Thwaites, Simon; Booth, David; Abbasnejad, Ehsan; Robertson, William; Thewlis, Dominic (2021): 力平台数据中用于基于步态的人物重识别的最具区分性成分。TechRxiv. 预印本。https://doi.org/10.36227/techrxiv.16683229.v1》相关的数据集和描述。数据集概述：本数据集系为研究步态作为（软）生物识别特征在人物重识别（re-ID）/识别中的应用而采集，但亦可用于解答众多研究问题。它是专为人物重识别而构建的最大的、最复杂的力量平台数据集之一。该数据集包含184名健康参与者完成的5327次行走试验，其中涉及服装、鞋类和行走速度的个体内和个体间变异，以及试验间个体间时间间隔的变异（数据收集跨越两个会话，会话间隔为3-14天不等）。数据集通过在阿德莱德大学步态分析实验室进行的重复测量实验（经人体研究伦理委员会批准 - 批准号 H-2018-009）生成。实验方案：在每次会话开始时，记录参与者的年龄、性别、体重、身高和鞋类类型，参与者穿着个人服装和鞋类。鞋类也被拍照以供日后参考。随后，参与者以自选的三个速度（首选速度、慢于首选速度和快于首选速度）沿实验室长度方向（约10米）单向行走五次。实验室中央的两台OPT400600-HP力平台（Advanced Mechanical Technology Inc., 美国）测量了左右脚步期间的地面反作用力（GRFs）和地面反作用矩（GRMs）。这些测量值以及计算出的支撑点坐标，通过Vicon Nexus（Vicon Motion Systems Ltd, 英国）以2000赫兹的频率采集。值得注意的是，本数据集中的所有试验均为完整足部接触；即每个足部完全位于每个力平台区域内，如视频片段所示。用户指南：实现本数据集的代码仓库可在GitHub - kayneduncanson1/ForceID-Study-1: 实现文章《深度度量学习在基于力平台数据的可扩展步态人物重识别中的应用》的代码仓库找到。数据集组织为单独的工作表，每个工作表包含来自特定力平台（命名为component_platform）的特定成分的所有样本（既有原始版本也有处理过的'pro'版本）。在每个数据工作表中，前五列包含关于每个试验的附加信息，随后是第六列及以后的列。在元数据工作表中包含ID编号及其相关的人口统计数据。第一列的ID编号来自为论文实现而实施的私有版本的数据集。这些编号可以与代码仓库中生成的公共数据集的ID编号进行交叉引用。这意味着文章补充材料-表VII（文章中）列出的具有挑战性的ID可以在此数据集中找到。