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Updated: Jul 31, 2025

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

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A double-channel multiscale depthwise separable convolutional neural network for abnormal gait recognition.

Xiaoguang Liu^1,2, Yubo Wu^1,2, Meng Chen^1,2

¹College of Electronic and Information Engineering, Hebei University, Baoding, China.

Mathematical Biosciences and Engineering : MBE

|May 10, 2023

Summary

This summary is machine-generated.

This study introduces a novel deep learning model for abnormal gait recognition, achieving 99.60% accuracy. The new method efficiently extracts gait features while significantly reducing computational costs and model size.

Keywords:

BK structure MDB abnormal gait recognition convolutional neural network (CNN)double-channel network

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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Area of Science:

Biomedical Engineering
Computer Science
Artificial Intelligence

Background:

Abnormal gait recognition is crucial for early disease detection and monitoring physical impairments.
Extracting detailed spatial features from abnormal gaits is challenging due to complex data.
High computational costs of existing models hinder practical applications.

Purpose of the Study:

To develop an efficient deep learning model for accurate abnormal gait recognition.
To extract fine-grained spatial features from abnormal gaits effectively.
To reduce the computational and memory footprint of gait recognition systems.

Main Methods:

Proposed a double-channel multiscale depthwise separable convolutional neural network (DCMSDSCNN).
Designed a multiscale depthwise feature extraction block (MDB) incorporating depthwise separable convolution (DSC) and Bottleneck (BK) structures.
Optimized feature extraction at multiple scales while minimizing network parameters.

Main Results:

Achieved a state-of-the-art gait recognition accuracy of 99.60%.
Reduced the model's memory size by 4.21 times compared to the unoptimized version.
Demonstrated effective extraction of subtle spatial features in abnormal gaits.

Conclusions:

The proposed DCMSDSCNN model offers a highly accurate and computationally efficient solution for abnormal gait recognition.
The novel MDB architecture successfully balances feature extraction capability and model complexity.
This approach holds significant potential for clinical applications in diagnosing and monitoring gait-related conditions.