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Home
Lightweight Visual Dynamic Gesture Recognition System Based On Cnn-lstm-dsa.

Home
Lightweight Visual Dynamic Gesture Recognition System Based On Cnn-lstm-dsa.

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Lightweight Visual Dynamic Gesture Recognition System Based on CNN-LSTM-DSA.

Zhenxing Wang¹, Ziyan Wu¹, Ruidi Qi¹

¹School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China.

Sensors (Basel, Switzerland)

|March 14, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study presents a lightweight visual dynamic gesture recognition system using a CNN-LSTM-DSA model for efficient deployment on embedded devices. The system achieves high accuracy for both static (96%) and dynamic (90.19%) gestures with low response delay.

Keywords:

CNN-LSTM hybrid model bionic robotic hand depthwise separable convolution visual gesture recognition

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

Large-scale gesture recognition models face challenges in computational complexity and embedded deployment.
Efficient and accurate gesture recognition is crucial for human-robot interaction and control systems.

Purpose of the Study:

To design and implement a lightweight visual dynamic gesture recognition system.
To address computational complexity and enable efficient deployment on embedded devices.
To achieve high-precision recognition of complex static and dynamic gestures.

Main Methods:

Utilized a lightweight Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-Dynamic Segment Assignment (DSA) model.
Employed MediaPipe for extracting 3D keypoint coordinates from camera-captured hand images.

Implemented joint angle calculation and sliding window smoothing for static gesture recognition.

Modeled keypoint time series using the CNN-LSTM-DSA hybrid model for dynamic gesture recognition.

Main Results:

Achieved static gesture recognition accuracy of up to 96% and dynamic gesture recognition accuracy of 90.19%.
Demonstrated robustness under various lighting and background conditions.
Maintained an overall response delay of less than 300 ms.
Ensured smooth servo motor angle mapping and stable robotic hand movements.

Conclusions:

The proposed lightweight CNN-LSTM-DSA system effectively overcomes challenges in gesture recognition model complexity and deployment.
The system offers a robust and accurate solution for both static and dynamic gesture recognition.
This approach facilitates efficient human-robot interaction and control applications on embedded systems.