Gait phase recognition method for lower limb exoskeleton robot based on SE channel attention mechanism enhanced TCN-SVM.

IF 1.7 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-04 DOI:10.1080/10255842.2025.2495251

BinHao Huang, Jian Lv, Ligang Qiang

引用次数: 0

Abstract

This study develops an integrated system for collecting kinematic signals from lower limb exoskeletons, combining thigh muscle pressure with inertial measurements. The system captures muscle pressure, triaxial acceleration, and angle data. A temporal convolutional network model with an SE attention mechanism and SVM classifier is proposed for gait phase recognition. Results show that the FMG-IMU data fusion strategy achieves high accuracy, stability, and low sensitivity to external noise, effectively recognizing gait phases and improving exoskeleton performance.

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基于SE通道注意机制的下肢外骨骼机器人步态相位识别方法增强了TCN-SVM。

本研究开发了一个集成系统，用于收集下肢外骨骼的运动学信号，将大腿肌肉压力与惯性测量相结合。该系统捕获肌肉压力、三轴加速度和角度数据。提出了一种结合SE注意机制和SVM分类器的时间卷积网络模型用于步态相位识别。结果表明，FMG-IMU数据融合策略具有较高的准确性、稳定性和较低的外部噪声敏感性，能够有效识别步态阶段，提高外骨骼性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.