计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 242-249.doi: 10.11896/jsjkx.201000019
王新平, 夏春明, 颜建军
WANG Xin-ping, XIA Chun-ming, YAN Jian-jun
摘要： 时间序列信号被广泛应用于各种模式识别的场合,针对大量目标的时间序列信号模式识别率低的问题,借助多种图像化手段,将时间序列信号转换为图像,采用图像分类算法实现模式识别。实验中采集了前臂上30种手语对应的肌音信号(Mechanomyography,MMG),将其转换为不同风格的图像,设计卷积神经网络(Convolution Neural Network,CNN)框架,对图像化的肌音信号训练集建立模式识别的分类模型,并且应用迁移学习(transfer learning)算法对模型进行多次优化,建立的分类模型识别率达98.7%,高于普通机器学习算法的识别率。实验结果证明了图像化处理时间序列信号可以有效提高多分类肌音信号模式识别的识别率,该研究可以为其他时间序列信号的模式识别研究提供参考。
中图分类号:
| [1]ZHANG C Z,GU X T,ZHANG Y M.Gesture RecognitionBased on Deep Convolutional Neural Network[J].Radio Engineering,2019,49(7):587-591. [2]KAKOTY N M,SHARMA M D.Recognition of Sign Language Alphabets and Numbers based on Hand Kinematics using a Data Glove[J].Procedia Computer Science,2018,133:55-62. [3]SURI K,GUPTA R.Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory[J].Computers & Electrical Engineering,2019,78:493-503. [4]LI L X,WANG Y,WU J J,et al.Micro-motion hand gesture recognition method based on improved multiple dimensional convolution neural network[J].Computer Engineering,2018,44(9):243-249. [5]AZAR S G,SEYEDARABI H.Trajectory-based recognition of dynamic Persian sign language using hidden Markov model[J].Computer Speech & Language,2020,61:101053. [6]WOŁCZOWSKI A,ZDUNEK R.Electromyography and mechanomyography signal recognition:Experimental analysis using multi-way array decomposition methods[J].Biocybernetics and Biomedical Engineering,2017,37(1):103-113. [7]ZHANG Y,XIA C.A preliminary study of classification of upper limb motions and forces based on mechanomyography[J/OL].Medical Engineering & Physics.https://www.researchgate.net/publication/341471100_A_preliminary_study_of_classification_of_upper_limb_motions_and_forces_based_on_mechanomyography. [8]XIE H B,ZHENG Y P,GUO J Y.Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control[J].Physiological Measurement,2009,30(5):441-457. [9]JIANG W D,XIA C M,ZHANG Y,et al.Static Fatigue Analysis of Upper Trapezius Muscle Based on Fractal Dimension of Mechanomyography[J].Space Medicine & Medicine Enginee-ring,2019(3):259-264. [10]YANG Y H,XIE H.Research on surface EMG signal gesture recognition based on convolution neural network [J/OL].Microcomputer and Applications.http://en.cnki.com.cn/Article_en/CJFDTotal-WXJY201715017.htm. [11]DING H,HE Q,ZENG L,et al.Motion intent recognition of individual fingers based on mechanomyogram[J].Pattern Recognition Letters,2017,88(1):41-48. [12]ALVES N,CHAU T.Uncovering patterns of forearm muscle activity using multi-channel mechanomyography[J].Journal of Electromyogr Kinesiol,2010,20(5):777-786. [13]WANG Z,OATES T.Encoding time series as images for visual inspection and classification using tiled convolutional neural networks[C]//Workshops at the twenty-ninth AAAI conference on artificial intelligence.2015:1. [14]YAN J J,CHEN S Y,YAN H X,et al.Wrist pulse analysis and recognition based on recurrent plot and convolution neural network[J].Computer Engineering and Applications,2020(7):26. [15]ZANGENEH E,RAHMATI M,MOHSENZADEH Y.Low re-solution face recognition using a two-branch deep convolutional neural network architecture[J].Expert Systems with Applications,2020,139:112854. [16]WANG Z,YAN W,OATES T.Time series classification from scratch with deep neural networks:A strong baseline[C]//2017 International Joint Conference on Neural Networks (IJCNN).IEEE,2017:1578-1585. [17]FAHIM S R,SARKER Y,SARKER S K,et al.Self attention convolutional neural network with time series imaging based feature extraction for transmission line fault detection and classification[J].Electric Power Systems Research,2020,187:106437. [18]SANAYHA M,VATEEKUL P.Remaining Useful Life Prediction Using Enhanced Convolutional Neural Network on Multivariate Time Series Sensor Data[J].Walailak Journal of Science and Technology (WJST),2019,16(9):669-679. [19]LIU S,DENG W.Very deep convolutional neural network based image classification using small training sample size[C]//2015 3rd IAPR Asian conference on pattern recognition (ACPR).IEEE,2015:730-734. [20]WANG S,ZHANG L,FU J.Adversarial transfer learning forcross-domain visual recognition[J].Knowledge-Based Systems,2020,204:106258. [21]ZHAO B,ZHANG X,ZHAN Z,et al.Deep multi-scale convolutional transfer learning network:A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains[J].Neurocomputing,2020,407:24-38. |
| [1] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [2] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
| [3] | 方义秋, 张震坤, 葛君伟. 基于自注意力机制和迁移学习的跨领域推荐算法 Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning 计算机科学, 2022, 49(8): 70-77. https://doi.org/10.11896/jsjkx.210600011 |
| [4] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
| [5] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [6] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [7] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
| [8] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
| [9] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
| [10] | 刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179 |
| [11] | 徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085 |
| [12] | 孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217 |
| [13] | 吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039 |
| [14] | 杨涵, 万游, 蔡洁萱, 方铭宇, 吴卓超, 金扬, 钱伟行. 基于步态分类辅助的虚拟IMU的行人导航方法 Pedestrian Navigation Method Based on Virtual Inertial Measurement Unit Assisted by GaitClassification 计算机科学, 2022, 49(6A): 759-763. https://doi.org/10.11896/jsjkx.211200148 |
| [15] | 张嘉淏, 刘峰, 齐佳音. 一种基于Bottleneck Transformer的轻量级微表情识别架构 Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer 计算机科学, 2022, 49(6A): 370-377. https://doi.org/10.11896/jsjkx.210500023 |
|
||
首页