UNB StepUP: A footStep database for gait analysis and recognition using Underfoot Pressure

2025-02-24

AI Summary

Existing plantar pressure datasets are limited in scale and accessibility, severely hindering gait recognition and biomechanical research. To address this, we introduce UNB StepUP-P150—the largest publicly available plantar pressure gait database to date—comprising over 200,000 gait samples from 150 subjects under diverse walking speeds and footwear conditions. It is the first standardized dataset featuring high spatial resolution (4 sensors/cm²) and an expansive sensing area (1.2 m × 3.6 m), acquired using high-density piezoelectric sensors under a unified protocol and rich multimodal annotations (subject ID, speed, footwear type). Leveraging this resource, deep learning models achieve >98% gait identification accuracy, establishing a new benchmark for pressure-based gait analysis. UNB StepUP-P150 advances contactless biometric authentication and clinical gait assessment in rehabilitation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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 extsuperscript{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.

Problem

• Lack of large gait pressure datasets
• Need for advanced gait analysis methods
• Exploring underfoot pressure for biometric recognition

Innovation

• High-resolution pressure sensing tiles
• Comprehensive gait pressure dataset
• Supports biometric recognition and deep learning