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

arXiv: https://arxiv.org/abs/2502.17244 · HTML: https://mlsanity.com/paper/2502.17244/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Gait Recognition Based on Deep Learning: A Survey](https://mlsanity.com/paper/2201.03323/) (2022)

## Built on by (most cited first, 3 of 3)

- [First International StepUP Competition for Biometric Footstep Recognition: Methods, Results and Remaining Challenges](https://mlsanity.com/paper/2602.11086/) (2026)
- [Dual-Path Region-Guided Attention Network for Ground Reaction Force and Moment Regression](https://mlsanity.com/paper/2512.05030/) (2025)
- [The 2nd International StepUP Competition for Biometric Footstep Recognition: From Steps to Strides](https://mlsanity.com/paper/2607.13905/) (2026)

## Abstract (arXiv)

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.