Abstract
Gait refers to the patterns of limb movement generated during walking, which are unique to each individual due to both physical and behavioural traits. Walking patterns have been widely studied in biometrics, biomechanics, sports, and rehabilitation. While traditional methods rely on video and motion capture, advances in underfoot pressure sensing technology now offer deeper insights into gait. However, underfoot pressures during walking remain underexplored due to the lack of large, publicly accessible datasets. To address this, the UNB StepUP database was created, featuring gait pressure data collected with high-resolution pressure sensing tiles (4 sensors/cm\textsuperscript{2}, 1.2m by 3.6m). Its first release, UNB StepUP-P150, includes over 200,000 footsteps from 150 individuals across various walking speeds (preferred, slow-to-stop, fast, and slow) and footwear types (barefoot, standard shoes, and two personal shoes). As the largest and most comprehensive dataset of its kind, it supports biometric gait recognition while presenting new research opportunities in biomechanics and deep learning. The UNB StepUP-P150 dataset sets a new benchmark for pressure-based gait analysis and recognition.
Abstract (translated)
步态是指在行走过程中肢体运动的模式，这种模式因个人的身体和行为特征而独一无二。步行模式已在生物识别学、生物力学、体育科学及康复医学等领域广泛研究。传统的方法主要依赖于视频分析和动作捕捉技术，但随着足下压力感应技术的进步，我们现在可以获得更加深入的步态洞察。然而，由于缺乏大规模且公开可访问的数据集，行走时的足下压力仍然没有得到充分的研究。 为了解决这一问题，新不伦瑞克大学（UNB）创建了StepUP数据库，该数据库收录了使用高分辨率的压力感应地砖采集到的步态压力数据（每平方厘米有4个传感器，尺寸为1.2米×3.6米）。StepUP的第一版，即UNB StepUP-P150，包括了来自150名个体超过20万次的脚步数据。这些数据涵盖了不同的步行速度（习惯性、缓慢至停止、快速和慢速）以及不同类型的鞋类（赤脚、标准鞋子和个人的两双鞋）。作为同类数据集中规模最大且最全面的数据集，UNB StepUP-P150支持生物识别步态识别，并为生物力学研究及深度学习提供了新的探索机会。该数据库确立了基于压力的步态分析和识别的新基准。
URL
https://arxiv.org/abs/2502.17244
