基于表面肌电信号的CNN-LSTM模型下肢动作识别研究
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新疆大学 智能制造现代产业学院

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TP301

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国家自然科学基金资助项目(52365039);自治区“天山英才”科技创新领军人才项目(2023TSYCLJ0051)


Lower Limb Motion Recognition Based on Surface Electromyography and a CNN-LSTM Fusion Model
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1.College of Intelligent Manufacturing Modern Industry,Xinjiang University,Urumqi;2.College of Intelligent Manufacturing Modern Industry,Xinjiang University,Urumqi,;3.China

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    摘要:

    为了提高对下肢运动的分类准确度,本文提出了一种基于表面肌电信号(sEMG)的卷积神经网络与长短期记忆网络融合识别模型(CNN-LSTM)。首先,采集了20名受试者进行上楼、下楼、行走和蹲起四种步态动作的sEMG;接着,对采集到的sEMG数据进行预处理,并提取了两种时域和频域特征,用作机器学习识别模型的特征输入;最后,基于预处理后肌电信号数据,构建了CNN-LSTM的下肢动作识别模型,并与CNN、LSTM和SVM模型的性能进行对比。结果显示,CNN-LSTM模型在下肢动作识别准确率上分别比CNN、LSTM和SVM模型高出2.16%、8.34%、和11.16%,证明了其优越的分类性能。为康复医疗器械与助力器械提供了一个有效的下肢运动功能改善方案。

    Abstract:

    To enhance the classification accuracy of lower limb movements, this paper introduces a hybrid recognition model based on surface electromyography (sEMG) that combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). Initially, sEMG data were collected from 20 subjects performing four types of gait movements: ascending stairs, descending stairs, walking, and squatting. Subsequently, the collected sEMG data underwent preprocessing, and both time domain and frequency domain features were extracted to serve as inputs for the machine learning recognition model. The CNN-LSTM model was then constructed for lower limb action recognition and compared against the performances of CNN, LSTM, and SVM models. The results demonstrate that the CNN-LSTM model outperforms the CNN, LSTM, and SVM models by 2.16%, 8.34%, and 11.16% in accuracy, respectively, thereby proving its superior classification performance. This model provides an effective solution for enhancing lower limb motor functions, offering significant benefits for rehabilitation medical equipment and power assist devices.

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周智伟,陶庆,刘景轩,等. 基于表面肌电信号的CNN-LSTM模型下肢动作识别研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-04-25
  • 最后修改日期:2024-05-24
  • 录用日期:2024-06-05
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