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

An Integrated Zero-Trust and Real-Time Detection Scheme for DDoS Protection in 5G IoT Systems.

Sensors (Basel, Switzerland)·2026
Same author

Denoising Respiratory Sinus Arrhythmia of Pulse-to-Pulse Interval Signals Extracted from Photoplethysmogram with an Autoregressive Moving Average Model.

Sensors (Basel, Switzerland)·2026
Same author

Precision improvement for indoor positioning based on fuzzy inference system with ultra-wideband wireless communications.

PloS one·2026
Same author

Hybrid CNN-LSTM Model for Evaluating Heart Rate Variability from Pulse-to-Pulse Intervals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Respiratory Rate Sensing for a Non-Stationary Human Assisted by Motion Detection.

Sensors (Basel, Switzerland)·2025
Same author

Supramolecular discrimination and diagnosis-guided treatment of intracellular bacteria.

Nature communications·2025
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

Related Experiment Video

Updated: Jun 27, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.8K

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Tan-Hsu Tan1, Jia-Hong Tian2, Alok Kumar Sharma3

  • 1Innovation Frontier Institute of Research for Science and Technology, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel human activity recognition (HAR) system using radar data and a deep learning model. The system achieves 98.2% accuracy, enhancing safety and quality of life through the Internet of Things (IoT).

Keywords:
cross-channel operationdeep learninghuman activity recognitionmicro-Doppler effectradar sensor

More Related Videos

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
Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible
14:44

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Published on: May 13, 2025

530

Related Experiment Videos

Last Updated: Jun 27, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.8K
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
Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible
14:44

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Published on: May 13, 2025

530

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Human Activity Recognition (HAR) is crucial for Internet of Things (IoT) applications, improving quality of life and safety.
  • Existing HAR systems often face limitations in accuracy and real-world applicability.
  • Radar-based sensing offers a promising, privacy-preserving approach for HAR.

Purpose of the Study:

  • To develop an advanced HAR system utilizing the micro-Doppler effect from radar data.
  • To enhance feature extraction and model performance for accurate activity recognition.
  • To validate the proposed system's effectiveness using a publicly available radar dataset.

Main Methods:

  • A two-stream one-dimensional Convolutional Neural Network (1D-CNN) combined with a Bidirectional Gated Recurrent Unit (BiGRU) was employed.
  • Short-Time Fourier Transform (STFT) processed radar data into time-frequency representations.
  • A novel Cross-Channel Operation (CCO) facilitated feature exchange between parallel convolutional layers.

Main Results:

  • The proposed 1D-CNN+CCO-BiGRU model achieved a high accuracy of 98.2% on the Rad-HAR dataset.
  • The system effectively extracted spatial and temporal features for robust activity recognition.
  • Demonstrated superior performance compared to existing HAR systems using radar sensors.

Conclusions:

  • The developed HAR system shows significant potential for real-world IoT applications.
  • The integration of 1D-CNN, BiGRU, and CCO offers a powerful approach for radar-based activity recognition.
  • This advancement contributes to the broader field of HAR within the IoT framework.