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Updated: Jun 17, 2026

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors.

Lucia Palazzo^1,2, Vladimiro Suglia², Sabrina Grieco²

¹Bioengineering Unit of Bari, Istituti Clinici Scientifici Maugeri IRCCS, Via Generale Bellomo, 73/75, 70124 Bari, Italy.

Sensors (Basel, Switzerland)

|January 11, 2025

Summary

This summary is machine-generated.

This study introduces a new method for pathological gait recognition (PGR) using deep learning on healthy subjects emulating abnormal walking. The approach shows promising accuracy and speed for early detection of gait disorders.

Keywords:

bioengineering convolutional neural network deep learning gait disorders gait recognition inertial measurement units rehabilitation

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Area of Science:

Biomedical Engineering
Neurology
Computer Science

Background:

Abnormal gait patterns can result from motor impairments or neurological conditions, posing safety risks.
Pathological gait recognition (PGR) aims to differentiate between various walking patterns.
Simulating gait disorders in healthy individuals offers a practical alternative to collecting actual pathological data for research.

Purpose of the Study:

To develop and evaluate a deep learning-based workflow for recognizing normal and pathological gaits using inertial data.
To assess the feasibility of using emulated gait disorders in healthy subjects for PGR model training.
To explore the potential of PGR in aiding early detection and rehabilitation tracking.

Main Methods:

A workflow utilizing convolutional neural networks (CNNs) was designed for gait analysis.
Inertial data was collected from nineteen healthy subjects exhibiting simulated abnormal walking patterns.
The CNN model was trained to classify between normal and pathological locomotor behaviors.

Main Results:

The developed system demonstrated promising accuracy in distinguishing between normal and pathological gaits.
The study highlighted the efficiency of the proposed method in terms of computational time.
Preliminary results suggest the potential for real-time application in clinical settings.

Conclusions:

The feasibility study indicates that deep learning models can effectively recognize emulated pathological gaits.
The approach offers a viable method for PGR research, potentially reducing experimental time and sample size requirements.
The findings support the future validation on actual pathological gait data for clinical applications in early detection and rehabilitation monitoring.