肌音信号模式识别在嵌入式实时手语翻译系统中的应用
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华东理工大学机械与动力工程学院,上海 200237

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Application of Mechanomyogram’s Pattern Recognition in Real-Time Sign Language Translation Embedded System
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Department of Mechanical Engineering, East China University of Science and Technology, Shanghai 200237, China

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    摘要:

    基于肌音信号(Mechanomyogram, MMG)的模式识别是指采集MMG信号,应用机器学习算法进行动作识别的过程。为了实现手语实时分类,本文采用基于STM32芯片的轻量级嵌入式设备,控制双轴加速度传感器采集了前臂3块肌肉的6通道MMG,应用反向传播神经网络(Back propagation neural network, BPNN)算法建立分类模型,并将模型参数导入嵌入式系统中,实现算法的移植。实验结果表明该嵌入式系统可实现30种手语的实时识别,模型自测识别率达99.6%,实时识别中可达97.5%,每个动作分类所需时间少于0.52 ms,满足实时性要求,具有较高的实际应用价值。本文的研究结果可应用于人体康复工程,哑语翻译器,义肢控制等领域。

    Abstract:

    Mechanomyogram (MMG)-based pattern recognition refers to the process of collecting MMG bands and applying machine learning algorithms to perform motion recognition. To realize the real-time classification of sign language motions, six-channel MMG signals of three muscles on forearm are collected by dual-axis acceleration sensors which are controlled by lightweight embedded device with STM32 chip. The back propagation neural network (BPNN) algorithm is used to establish recognition classification models, where the parameters are extracted and put into the embedded system to transfer BPNN algorithm. The embedded device can accomplish real-time recognition of 30 kinds of sign language motions, with the self-test accuracy up to 99.6% and the accuracy of real-time recognition up to 97.5%. Moreover, the classification time for each motion is less than 0.52 ms, satisfying the real-time recognition condition. The results can be applied to the fields of rehabilitation engineering, sign language translator, and prosthetic control, etc.

    表 1 模型自测结果Table 1 Self-test results
    表 3 多种分类器自测识别率对比Table 3 Self-test of different classifiers
    图1 技术路线流程图Fig.1 Flow chart of technical process
    图2 常见的采集形式Fig.2 Commonly-used data collector
    图3 环形弹性带采集装置Fig.3 Elastic bracelet data acquisition device
    图4 金属表带结构采集装置Fig.4 Metal braclet data acquisition device
    图5 基于指数加权平均的能量阈值分割算法Fig.5 Energy threshold segmentation algorithm based on exponential weighted average
    图6 NN模型框架(3层隐含层,神经元个数分别为80,50,30)Fig.6 NN architecture (three hidden layers with neuron numbers of 80, 50 and 30)
    图7 采集手语动作样本Fig.7 Collection of sign language motion data
    图8 传感器对应的前臂三块肌肉Fig.8 Three muscles of the forearm corresponding to sensors
    图9 30种中国标准手势语言Fig.9 30 kinds of Chinese sign language gestures
    图10 志愿者1实时测试结果混淆矩阵Fig.10 Confusion matrix of volunteer 1
    表 2 模型实时识别测试Table 2 Real-time classification accuracy
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引用本文

王新平,夏春明,章含阳.肌音信号模式识别在嵌入式实时手语翻译系统中的应用[J].数据采集与处理,2021,36(2):304-313

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  • 收稿日期:2020-08-05
  • 最后修改日期:2021-01-17
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  • 在线发布日期: 2021-04-15