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Updated: Jul 10, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

基于深度学习的识别算法,用于在行走环境之间的过渡,仅使用电肌图信号.

Pankwon Kim, Jinkyu Lee, Jiyoung Jeong

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

|November 23, 2023

概括

此摘要是机器生成的。

这项研究开发了一个深度学习算法,仅使用电肌图 (EMG) 信号来准确地分类不同步行地形之间的过渡. 该模型实现了95.4%的准确性,使步行辅助设备能够更好地控制.

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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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科学领域:

生物医学工程生物医学工程
机器人技术机器人技术机器人技术
人与计算机的交互

背景情况:

对于行走辅助设备的现有地形识别通常依赖于传感器融合.
仅使用电肌图 (EMG) 信号专门对步态转变进行分类的研究是有限的.

研究的目的:

仅使用EMG信号,为各种行走环境之间的过渡提出识别算法.
为了利用深度学习对基于下肢肌肉活动的步态环境变化进行分类.

主要方法:

在不同地形 (平面,楼梯,斜坡) 和过渡中,测量了27名受试者的EMG信号,跨越多个下肢肌肉.
使用人工神经网络 (ANN) 模型,在立场阶段输入整个EMG配置文件.
使用所有肌肉激活和关键肌肉群的子集来评估分类准确性.

主要成果:

该ANN模型实现了95.4%的高分类准确度,用于识别使用所有测量的肌肉激活来识别行走环境之间的过渡.
减少了一组肌肉激活 (膝盖伸缩器,脚伸缩器,大足曲器) 的分类准确率为90.9%.

结论:

不同步行环境之间的过渡可以准确地识别使用只有EMG信号在立场阶段.
开发的ANN模型展示了精确步态转变分类的潜力,为先进的行走辅助设备铺平了道路.