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 Experiment Video

Updated: May 14, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A Privacy-Preserving Artificial Intelligence-Driven Sensing System for Distributed Multimodal Risk Detection.

Yawen Zhu1, Yiwei Song1,2, Yikun Xuan2

  • 1China Agricultural University, Beijing 100083, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

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

An automated, digital immunoassay on a microfluidic cartridge for on-demand cytokine profiling.

Microsystems & nanoengineering·2026
Same author

A Multimodal Sensor-Based Self-Supervised Learning Framework for Low-Noise System State Prediction and Anomaly Detection.

Sensors (Basel, Switzerland)·2026
Same author

An AI-Driven Multimodal Sensing Framework Integrating UAV Imagery and Environmental Sensors for Intelligent Farmland Monitoring.

Sensors (Basel, Switzerland)·2026
Same author

A next generation of the schema therapy model of personality pathology: A cross-cultural and international study protocol.

PloS one·2026
Same author

Does gender moderate the relationship between bullying victimization and depression? A longitudinal study.

BMC psychology·2026
Same author

[<i>Chonggu</i> Granules attenuate cartilage injury in knee osteoarthritis patients with liver-kidney deficiency syndrome by regulating the Nrf2 pathway].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026
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

This study introduces a federated multimodal security perception framework (FMS-LLM) for intelligent security. The FMS-LLM framework enhances security analysis by fusing multimodal data, improving accuracy and robustness in distributed environments.

Area of Science:

  • Artificial Intelligence
  • Cybersecurity
  • Machine Learning

Background:

  • Intelligent terminals, mobile payments, and IoT devices necessitate advanced security beyond traditional analysis.
  • Distributed security systems face challenges with multimodal data heterogeneity and privacy constraints.
  • Existing methods struggle with non-independent and identically distributed (Non-IID) data across nodes.

Purpose of the Study:

  • To propose an artificial intelligence-driven federated multimodal security perception framework (FMS-LLM).
  • To address challenges in distributed security perception, including data heterogeneity, Non-IID data, and privacy.
  • To enhance security analysis through intelligent perception of user behavior, device states, and environmental context.

Main Methods:

Keywords:
artificial intelligence-driven sensingcross-modal semantic reasoningfederated learningmultimodal sensingprivacy-preserving risk detection

Related Experiment Videos

Last Updated: May 14, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

  • Developed a federated multimodal security perception framework (FMS-LLM).
  • Introduced a Non-IID adaptive federated fusion mechanism for dual-level alignment (structural and semantic).
  • Integrated an LLM-driven semantic enhancement module for mapping low-level features to high-level risk representations.

    • Main Results:

    • Achieved high performance: 91.62% Accuracy, 90.70% F1-score, and 94.73% ROC-AUC.
    • Demonstrated strong robustness under Non-IID conditions (88.47% Accuracy, 87.11% F1-score at α=0.1).
    • Reduced communication cost to 18.92 MB/Round while maintaining performance.

    Conclusions:

    • The FMS-LLM framework effectively fuses multi-source sensing information under privacy preservation.
    • The proposed method supports intelligent security perception with high accuracy, robustness, and interpretability.
    • FMS-LLM outperforms baseline methods in distributed multimodal security perception tasks.