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: May 28, 2026

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

A Single-Antenna RFID Machine Learning Approach for Direction and Orientation Tracking in Industrial Logistics.

João M Faria^1,2, Luis Vilas Boas^1,2, Joaquin Dillen^1,2

¹2Ai-School of Technology, IPCA, 4750-810 Barcelos, Portugal.

Sensors (Basel, Switzerland)

|May 27, 2026

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

Editorial: Biomechanics and mechanotransduction in cardiovascular calcification.

Frontiers in cardiovascular medicine·2026

Same author

Nationwide seroepidemiology of canine exposure to Ehrlichia canis and Babesia spp. in Portugal (2015-2025): One Health biosentinel surveillance.

Parasites & vectors·2026

Same author

Seroprevalence and associated factors of Leishmania infantum in small ruminants in Portugal.

Parasites & vectors·2026

Same author

Silent shedding of zoonotic enteric protozoa in domestic pigs for human consumption in selected farms in the Iberian Peninsula.

Veterinary research communications·2026

Same author

Diagnostic potential of a multi-antigen ELISA for feline leishmaniosis.

Parasites & vectors·2026

Same author

Molecular Detection of <i>Bartonella henselae</i> in Healthy Cats from Portugal (2015-2025): One Health Context and Implications for Transfusion Medicine.

Pathogens (Basel, Switzerland)·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 summary is machine-generated.

This study introduces a single-antenna Radio Frequency Identification (RFID) system for industrial logistics, achieving over 97% accuracy in direction tracking under challenging shop floor conditions using physics-informed domain adaptation.

Area of Science:

Robotics and Automation
Signal Processing
Machine Learning

Background:

Industry 4.0 logistics increasingly rely on Radio Frequency Identification (RFID) for low-cost tracking.
Current RFID direction/orientation estimation often requires multiple antennas and lacks robustness in industrial settings.
Industrial environments present challenges like multipath fading, operator variability, and signal fragmentation.

Purpose of the Study:

To develop and evaluate a single-antenna RFID system for robust direction and orientation estimation in Industry 4.0 logistics.
To investigate the effectiveness of various machine learning architectures and a physics-informed augmentation strategy.
To address the domain gap between laboratory and industrial shop floor environments.

Main Methods:

Keywords:

Internet of Things (IoT)Radio Frequency Identification (RFID)deep learning (DL)domain adaptation logistics machine learning (ML)tracking

Related Experiment Videos

Last Updated: May 28, 2026

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

Proposed a single-antenna RFID system processing Received Signal Strength Indicator (RSSI) and phase data.
Evaluated thirteen architectures: unsupervised (clustering), supervised (classical ML, deep learning, hybrid).
Implemented a physics-informed augmentation strategy for synthetic data generation and domain adaptation using XGBoost.

Main Results:

Laboratory experiments achieved >99.5% accuracy for direction and orientation tasks.
Domain adaptation with XGBoost recovered direction accuracy to >97% under severe fragmentation and Non-Line-of-Sight (NLoS) conditions.
Shop floor accuracy exceeded prior single-antenna studies (75-92%) after domain adaptation.

Conclusions:

Single-antenna RFID with physics-informed domain adaptation offers a promising solution for Industrial Internet of Things (IIoT) logistics.
The proposed system demonstrates robustness against industrial environmental challenges.
Physics-informed domain adaptation effectively bridges the domain gap for real-world applications.