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

Lightweight Detection of Inserted Chirp Symbols in Radio Transmission from Commercial UAVs.

Sensors (Basel, Switzerland)·2025
Same author

Software-Defined NB-IoT Uplink Framework-The Design, Implementation and Use Cases.

Sensors (Basel, Switzerland)·2021
Same author

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning.

Sensors (Basel, Switzerland)·2021
Same author

Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes.

Sensors (Basel, Switzerland)·2020
Same author

Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks.

Sensors (Basel, Switzerland)·2019

Related Experiment Video

Updated: Jun 28, 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

3.7K

User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep

Sebastian Urwan1, Krzysztof K Cwalina2

  • 1Intel Technology Poland Sp. z o.o., 80-298 Gdańsk, Poland.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary

Deep learning accurately detects user position and orientation in ultra-wideband wireless body area networks (WBANs). This method significantly outperforms traditional techniques for precise device placement and angle estimation.

Keywords:
LoSNLoSUWBbody area networksdeep learningorientation

More Related Videos

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

7.0K
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.3K

Related Experiment Videos

Last Updated: Jun 28, 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

3.7K
Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

7.0K
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.3K

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Biomedical Engineering

Background:

  • Ultra-wideband (UWB) technology is crucial for Wireless Body Area Networks (WBANs).
  • Accurate user positioning relative to antenna geometry is essential for reliable off-body WBAN communication.
  • Existing methods for user localization in WBANs have limitations in precision and efficiency.

Purpose of the Study:

  • To investigate the efficacy of deep learning methods for detecting user position and orientation in UWB off-body WBANs.
  • To compare the performance of neural networks against traditional threshold methods for user classification.
  • To develop and validate a deep learning approach for estimating user position angles relative to antenna geometry.

Main Methods:

  • Developed a measurement stand using EVB1000 devices and DW1000 radio modules for UWB channel impulse response measurement.
  • Conducted indoor static measurement scenarios.
  • Applied deep learning models, including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs), for classification and angle estimation.

Main Results:

  • Neural networks achieved over 9% higher accuracy than threshold methods for binary classification of user orientation.
  • The proposed deep learning approach successfully estimated user position angles relative to antenna geometry using channel impulse response.
  • Achieved absolute user orientation angle errors of approximately 4-7° for CNNs and 14-15° for MLPs in 85% of cases.

Conclusions:

  • Deep learning methods offer superior performance for user position and orientation detection in UWB off-body WBANs.
  • The developed approach enables precise angle estimation, crucial for advanced WBAN applications.
  • CNNs demonstrate higher accuracy in angle estimation compared to MLPs in the tested scenarios.