A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds

TL;DR

This paper introduces the UNB StepUP-P150 dataset, a large, high-resolution plantar pressure dataset from 150 individuals, enabling advanced gait analysis, recognition, and biomechanics research across various walking speeds and footwear conditions.

Contribution

The paper presents a comprehensive, publicly accessible plantar pressure dataset with high-resolution data from 150 individuals, supporting gait analysis and recognition research.

Findings

Over 200,000 footsteps collected

Supports gait recognition across different speeds and footwear

Establishes a new benchmark for plantar pressure analysis

Abstract

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.