Search research articles

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related 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: Jul 20, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Smartphone Authentication System Using Personal Gaits and a Deep Learning Model.

Jiwoo Choi¹, Sangil Choi¹, Taewon Kang¹

¹Department of Computer Science and Engineering, Gangneung-Wonju National University, Wonju 26403, Republic of Korea.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study introduces a novel smartphone authentication system using human gait, achieving over 90% accuracy. This gait analysis method significantly reduces data collection time for reliable user identification.

Keywords:

authentication convolutional neural network human gait machine learning

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Related Experiment Videos

Last Updated: Jul 20, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Computer Science
Biometrics
Human-Computer Interaction

Background:

Secure information sharing is vital in hyper-connected societies.
Traditional smartphone authentication methods require improvement for enhanced security.
Identifying unauthorized users is critical for protecting sensitive data.

Purpose of the Study:

To propose a novel smartphone authentication system leveraging human gait.
To develop a deep learning model for gait feature extraction.
To validate the system's reliability and efficiency for user authentication.

Main Methods:

A convolutional neural network (CNN) deep learning model was employed to learn human gait features.
The trained model was integrated into a smartphone for real-time authentication.
Users authenticated by walking for 1.8 seconds while carrying the smartphone.

Main Results:

The proposed gait authentication system achieved an average accuracy, precision, recall, and F1-score of at least 90%.
Gait data collection time was reduced from 7 seconds to 1.8 seconds, a four-fold enhancement.
The system demonstrated high reliability in identifying legitimate users.

Conclusions:

Human gait can be effectively utilized as a new biometric authentication method for smartphones.
The developed system offers a secure and efficient alternative to traditional authentication techniques.
Short-duration gait data is sufficient for robust user authentication, showcasing significant performance improvements.