构建150人高分辨率足底压力数据集，支持步态识别与生物力学研究。
A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds
- 采集150人足底压力数据，每平方厘米4个传感器，覆盖多种鞋履与速度
- 含超20万步数据，支持裸足、普通鞋、自选鞋及四种步行速度分析
- 为步态识别和深度学习提供新基准，适合生物力学与可穿戴研究者
步态指行走时肢体运动的模式，因个体生理与行为特征而异。步态研究广泛应用于生物识别、生物力学、体育科学和康复领域。传统方法依赖视频与运动捕捉，而足底压力传感技术的进步为步态分析提供了更深层洞察。然而，由于缺乏大规模公开数据集，足底压力研究仍不充分。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名参与者的数据。该数据集采用1.2m×3.6m的压力感应走道，采集分辨率达每平方厘米4个传感器的高分辨率足底压力数据，涵盖超过20万次步态记录，涉及不同步行速度（自然、慢速至停止、快速、慢速）和鞋履条件（赤足、标准鞋、两双个人鞋）。该数据集推动了基于足底压力的步态识别发展，为生物力学与深度学习研究提供新机遇，确立了新的基准。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。