Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Comparison of the effects of acupuncture and drug treatment for central post-stroke pain: A systematic review and network meta-analysis of randomized trials.

Behavioural brain research·2025
Same author

Correction: Pharmacologic inhibition of CSF-1R suppresses intrinsic tumor cell growth in osteosarcoma with CSF-1R overexpression.

Journal of translational medicine·2025
Same author

Pharmacologic inhibition of CSF-1R suppresses intrinsic tumor cell growth in osteosarcoma with CSF-1R overexpression.

Journal of translational medicine·2025
Same author

Discovery and preclinical evaluations of TQB3616, a novel CDK4-biased inhibitor.

Bioorganic & medicinal chemistry letters·2024
Same author

Correction to: Can fintech pave the way for a transition towards low-carbon economy? Examination based on machine learning algorithm.

Environmental science and pollution research international·2024
Same author

Can fintech pave the way for a transition towards low-carbon economy? Examination based on machine learning algorithm.

Environmental science and pollution research international·2024

Related Experiment Video

Updated: Oct 27, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

4.6K

Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices.

Xin Zeng1, Xiaomei Zhang1, Shuqun Yang1

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Sensors (Basel, Switzerland)
|July 20, 2021
PubMed
Summary

Edge computing-based mobile Device Implicit Authentication (EDIA) uses gait biometrics for secure mobile access. This deep learning approach achieves high accuracy, even with limited data, enhancing mobile security.

Keywords:
LSTMconvolutional neural networkedge computinggait recognitionimplicit authentication

More Related Videos

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

867
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.7K

Related Experiment Videos

Last Updated: Oct 27, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

4.6K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

867
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.7K

Area of Science:

  • Cybersecurity
  • Mobile Computing
  • Biometrics

Background:

  • Implicit authentication for mobile devices faces challenges with accuracy and resource constraints.
  • Behavioral biometrics show insufficient accuracy for robust security.
  • Mobile devices have limited storage and energy, impacting data processing.

Purpose of the Study:

  • To propose an implicit authentication architecture for mobile devices using edge computing.
  • To enhance mobile security and privacy through gait biometric identification.
  • To address the limitations of current behavioral biometrics on mobile platforms.

Main Methods:

  • Developed Edge computing-based mobile Device Implicit Authentication (EDIA) architecture.
  • Utilized gait data from accelerometer and gyroscope sensors.
  • Employed a deep learning model (CNN and LSTM) with 2D gait signal image features.
  • Offloaded model generation to the cloud and authentication to edge devices.

Main Results:

  • Achieved a 97.77% true positive rate and a 2% false positive rate.
  • Demonstrated high accuracy even with limited dataset sizes.
  • EDIA effectively authenticates users implicitly using gait biometrics.

Conclusions:

  • EDIA offers a viable solution for secure implicit authentication on mobile devices.
  • The edge computing approach mitigates resource constraints on mobile devices.
  • Gait biometrics combined with deep learning provide a promising security mechanism.