Gait refers to the patterns of limb movement generated during walking, which are unique to each individual due to both physical and behavioral traits. Walking patterns have been widely studied in biometrics, biomechanics, sports, and rehabilitation. While traditional methods rely on video and motion capture, advances in plantar 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, we introduce the UNB StepUP-P150 dataset: a footStep database for gait analysis and recognition using Underfoot Pressure, including data from 150 individuals. This dataset comprises high-resolution plantar pressure data (4 sensors per cm-squared) collected using a 1.2m by 3.6m pressure-sensing walkway. It contains over 200,000 footsteps from participants walking with various speeds (preferred, slow-to-stop, fast, and slow) and footwear conditions (barefoot, standard shoes, and two personal shoes), supporting advancements in biometric gait recognition and presenting new research opportunities in biomechanics and deep learning. UNB StepUP-P150 establishes a new benchmark for plantar pressure-based gait analysis and recognition.

翻译：步态指行走过程中产生的肢体运动模式，由于生理与行为特征的独特性，每个个体的步态均具有特异性。行走模式在生物识别、生物力学、运动科学及康复医学领域已被广泛研究。传统方法主要依赖视频与动作捕捉技术，而足底压力传感技术的进步为步态研究提供了更深入的洞察。然而，由于缺乏大规模公开可用的数据集，行走过程中的足底压力研究仍显不足。为此，我们推出UNB StepUP-P150数据集：一个基于足底压力的步态分析与识别数据库，包含150名受试者的数据。该数据集通过1.2米×3.6米压力传感步道采集高分辨率足底压力数据（每平方厘米4个传感器），涵盖受试者在不同步行速度（舒适、慢速至停止、快速、慢速）与鞋履条件（赤足、标准鞋、两双个人鞋）下行走产生的超过20万次足步记录。本数据集不仅推动生物特征步态识别技术的发展，更为生物力学与深度学习领域提供了新的研究机遇。UNB StepUP-P150为基于足底压力的步态分析与识别建立了新的基准。