Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Nov 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Unsupervised and scalable low train pathology detection system based on neural networks.

Jorge Sanchez-Casanova¹, Judith Liu-Jimenez¹, Paloma Tirado-Martin¹

¹University Group for ID technologies (GUTI), University Carlos III of Madrid, Spain.

|March 4, 2021

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features.

Sensors (Basel, Switzerland)·2021

Same author

Wearable Transcranial Ultrasound System for Remote Stimulation of Freely Moving Animal.

IEEE transactions on bio-medical engineering·2020

Same author

Wrist Vascular Biometric Recognition Using a Portable Contactless System.

Sensors (Basel, Switzerland)·2020

Same author

Mobile Wireless Low-intensity Transcranial Ultrasound Stimulation System for Freely Behaving Small Animals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020

Same author

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones.

Sensors (Basel, Switzerland)·2019

Same author

Correction: Biometrics: Accessibility challenge or opportunity?

PloS one·2018

This study introduces a novel Neural Network (NN) system for detecting lower body pathologies through gait analysis. The system achieves 92% accuracy in classifying walking abnormalities without retraining for new users.

Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Gait Analysis

Background:

Existing medical technologies often overlook lower body pathologies.
Gait analysis offers a non-invasive method for assessing lower body health.

Purpose of the Study:

To develop a Neural Network (NN)-based system for classifying lower body pathologies using gait.
To enable the system to adapt to new users without complete retraining.

Main Methods:

Filtering and processing signals to extract Gait Cycles (GCs).
Utilizing GCs as input for the NN.
Employing random search optimization for network tuning.

Main Results:

The system achieved 92% accuracy in classifying lower body pathologies.

Keywords:

Biomechanics Gait analysis Pathology detection Pattern recognition Recurrent neural network Signal processing

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Nov 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Effective classification was obtained using 60% of the training data.

The system demonstrated adaptability to new users.

Conclusions:

The developed NN system provides a promising first-stage detection method for lower body pathologies.
This approach allows for pathology detection in uncontrolled environments without requiring specialized facilities.