基于改进深度森林的表面肌电手势识别方法

黄圣; 茅健

doi:10.12299/jsues.22-0173

基于改进深度森林的表面肌电手势识别方法

doi: 10.12299/jsues.22-0173

黄圣,
茅健^,

上海工程技术大学机械与汽车工程学院，上海 201620

详细信息

作者简介:
黄圣：黄　圣（1996 −），男，在读硕士，研究方向为智能控制. E-mail: hshengp@qq.com

通讯作者:
茅　健（1972 −），男，教授，博士，研究方向为检测与控制、智能机器人. E-mail: jmao@sues.edu.cn

中图分类号: TP181; TP391.4
计量
- 文章访问数: 329
- HTML全文浏览量: 150
- PDF下载量: 516
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-31
- 刊出日期: 2023-06-20

Recognition method of sEMG gesture based on improved deep forest

HUANG Sheng,
MAO Jian^,

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

摘要

摘要: 为提高基于表面肌电图（surface Electromyo Graphy, sEMG）手势识别的准确率，提出一种改进深度森林相结合的手部运动识别方法. 将极致梯度提升（eXtreme Gradient Boosting, XGBoost）树引入深度森林模型，与随机森林和完全随机森林共同组成深度森林的级联结构. 深度森林模型在每个层次上集成3种不同的基于树的分类器，共4个决策森林，包括1个随机森林、1个极端随机森林和2个极致梯度提升树，利用不同学习算法之间的互补性来提高分类性能. 为评估该模型性能，采集4名健康受试者的表面肌电信号进行手部动作识别验证试验，并与随机森林、支持向量机、一维卷积神经网络及二维卷积神经网络等算法比较. 结果表明，提出方法对16种常用手部动作的平均识别精度为94.14%，对表面肌电信号实现了较高的分类准确率.
- 深度森林 /
- 表面肌电图 /
- 深度学习 /
- 模式识别 /
- 极致梯度提升 /
- 动作分类
Abstract: In order to improve the accuracy of gesture recognition based on surface electromyography (sEMG), an improved deep forest combined hand motion recognition method was proposed. The extreme gradient boosting (XGBoost) tree was introduced into the deep forest model to form the cascade structure of deep forest together with the random forest and the complete random forest. The deep forest model integrates three different tree-based classifiers at each level, a total of four decision forests including a random forest, an extreme random forest and two extreme gradient boosting trees. The classification performance was improved by using the complementarity between different learning algorithms. In order to evaluate the performance of the model, the sEMG signals of 4 healthy subjects were collected for the verification experiment of hand action recognition, and compared with random forest, support vector machine, one-dimensional and two-dimensional convolutional neural networks algorithms. The result shows that the average recognition accuracy of the method for 16 commonly used hand actions is 94.14%, and the classification accuracy of sEMG signals is high.
- deep forest /
- surface electromyography (sEMG) /
- deep learning /
- pattern recognition /
- extreme gradient boosting (XGBoost) /
- action classification

HTML全文

图 1 改进后的深度森林模型

Figure 1. Improved deep forest model

下载: 全尺寸图片幻灯片

图 2 基于改进深度森林手部动作识别流程图

Figure 2. Flowchart of hand motion recognition based on improved deep forest

下载: 全尺寸图片幻灯片

图 3 不同滑动窗口的特征识别结果

Figure 3. Feature recognition results for different sliding windows

下载: 全尺寸图片幻灯片

图 4 不同滑动窗口的特征提取时间

Figure 4. Feature extraction time for different sliding windows

下载: 全尺寸图片幻灯片

表 1 本方法与其他方法识别准确率结果对比

Table 1. Recognition accuracy of this method compared with others %

受试者编号	GC_Forest	RF	SVM	1D-CNN	2D-CNN	TextCNN
1	97.47	91.44	89.17	87.46	90.43	90.38
2	95.39	92.69	93.31	88.29	93.41	90.70
3	91.61	89.14	87.89	85.78	88.50	87.77
4	93.08	87.68	86.84	84.01	88.92	88.81
Ave	94.14	90.54	89.30	86.39	90.32	89.42

下载: 导出CSV

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