ForceID Dataset A

<br><b>This version (Version 6) is the dataset associated with the article, </b><b>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. </b><b><i>Sensors</i></b><b> 2023, </b><b><i>23</i></b><b>, 3392. https://doi.org/10.3390/s23073392. </b>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.<br><b>Dataset overview</b>This 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.<br><b>Experimental protocol</b>At 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.<br><b>User guide</b>The 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（版本6）是与以下论文关联的数据集：Duncanson, K.A.; Thwaites, S.; Booth, D.; Hanly, G.; Robertson, W.S.P.; Abbasnejad, E.; Thewlis, D. 《基于力平台数据的可扩展步态人员重识别深度度量学习》（Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data）。*Sensors* 2023, *23*, 3392. https://doi.org/10.3390/s23073392。如需查阅与2021年论文相关的数据集及描述，请参见版本3：Duncanson, Kayne; Thwaites, Simon; Booth, David; Abbasnejad, Ehsan; Robertson, William; Thewlis, Dominic (2021): 《基于力平台数据的步态人员重识别最优判别分量》（The Most Discriminant Components of Force Platform Data for Gait Based Person Re-identification）。TechRxiv预印本。https://doi.org/10.36227/techrxiv.16683229.v1。 ### 数据集概述 本数据集专为将步态作为（软）生物特征（soft biometric）用于人员重识别（Person Re-Identification, Re-ID）/身份识别的研究采集，但也可用于解答各类研究问题。它是专为人员重识别构建的规模最大、结构最复杂的力平台数据集之一，包含来自184名健康受试者的5327次行走测试。数据涵盖个体间与个体内的衣着、鞋类、行走速度差异，以及不同测试间的时间间隔差异（数据分两次采集，两次间隔为3至14天，依受试者个体情况而定）。该数据集通过重复测量实验生成，该实验已获得阿德莱德大学人类研究伦理委员会批准（批准号：H-2018-009），采集于阿德莱德大学步态分析实验室。 ### 实验方案 每次测试开始时，记录受试者的年龄、性别、体重、身高以及鞋类类型，此时受试者身着日常衣物与个人鞋履，同时拍摄鞋履照片留档。随后，受试者沿实验室通道（≈10米）单向行走，分别以三种自选速度（自然步速、慢于自然步速、快于自然步速）各行走5次。实验室中央布置两台埋入式OPT400600-HP力平台（Advanced Mechanical Technology Inc., 美国），用于采集左右足踩踏时的地面反作用力（Ground Reaction Forces, GRFs）与地面反作用力矩（Ground Reaction Moments, GRMs）。上述数据与计算得到的中心压力（Center of Pressure, COP）坐标均通过Vicon Nexus（Vicon Motion Systems Ltd, 英国）以2000Hz的采样率采集。需特别说明的是，本数据集的所有测试均为完整足触地测试，即通过影像资料确认每只脚均完全踩踏在力平台的有效区域内。 ### 用户指南 本数据集的实现代码仓库可在GitHub - kayneduncanson1/ForceID-Study-1 获取，对应文章《基于力平台数据的可扩展步态人员重识别深度度量学习》。数据集按独立电子表格组织，每个电子表格包含来自指定力平台的某一特定分量的全部样本（命名格式为component_platform）。同时提供原始数据与经过处理的（"pro"版）数据。每个数据电子表格的前5列为单次测试的附属信息，第6列及之后为测试数据。元数据电子表格包含受试者ID编号及其相关人口统计学信息。第一列的ID编号对应论文使用的私有数据集版本，可与代码仓库中公开数据集生成的ID编号相互对照。这意味着论文补充材料表VII中列出的具有挑战性的测试ID可在本数据集中找到。