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

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

Intercomparisons of computed epithelial/absorbed power density and temperature rise in anatomical human face models under localized exposures at 10 GHz and 30 GHz.

Physics in medicine and biology·2025

Same author

Simultaneous classification and delineation of seven types of histological growth patterns in lung adenocarcinomas using self-supervised learning and online hard patch mining.

Quantitative imaging in medicine and surgery·2025

Same author

A Deep Learning Framework for Evaluating the Over-the-Air Performance of the Antenna in Mobile Terminals.

Sensors (Basel, Switzerland)·2024

Same author

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results.

Sensors (Basel, Switzerland)·2023

Same author

A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color Fluorescence In Situ Hybridization (FISH) Image and Deep Refined Learning.

Journal of digital imaging·2023

Same author

Evaluation of Chinese populational exposure to environmental electromagnetic field based on stochastic dosimetry and parametric human modelling.

Environmental science and pollution research international·2023

Search research articles

Related Experiment Video

Updated: Mar 29, 2026

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

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

Published on: March 28, 2025

CBAM-Enhanced CNN-LSTM with Improved DBSCAN for High-Precision Radar-Based Gesture Recognition.

Shiwei Yi¹, Zhenyu Zhao¹, Tongning Wu¹

¹China Academy of Information and Communications Technology, Beijing 100191, China.

Sensors (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

This study introduces the CECL framework for accurate radar-based gesture recognition. The novel approach significantly improves performance in complex environments, achieving 98.33% accuracy.

Keywords:

CBAM-enhanced CNN-LSTM (CECL)clutter suppression gesture recognition millimeter wave radar wavelet threshold algorithm

More Related Videos

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

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

Published on: December 15, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Mar 29, 2026

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

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

Published on: March 28, 2025

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

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

Published on: December 15, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Science
Signal Processing
Artificial Intelligence

Background:

Radar-based gesture recognition is vital for industrial and daily applications.
Complex scenarios present challenges like clutter, similar gestures, and ambiguous features, limiting current algorithm performance.

Purpose of the Study:

To propose a novel framework, CECL, for high-accuracy and robust radar-based gesture recognition.
To enhance spatial-temporal feature extraction and clutter suppression for improved performance.

Main Methods:

A Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture integrated with the Convolutional Block Attention Module (CBAM).
Signal processing techniques including Blackman window for spectral leakage suppression, wavelet thresholding, and dynamic energy nulling for clutter reduction.
An improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for noise elimination.

Main Results:

The CECL framework achieved an average accuracy of 98.33% in gesture classification, surpassing baseline models.
Demonstrated excellent recognition performance across varying distances and angles, indicating enhanced robustness.

Conclusions:

The proposed CECL framework effectively addresses challenges in radar-based gesture recognition.
Achieves state-of-the-art accuracy and robustness, making it suitable for complex real-world applications.