Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network
Time series signals are widely used in various pattern recognition applications.In order to solve the problem of low pattern recognition rate of time series signals for a large number of targets,this article uses a variety of transform methods to convert time series signals into images,and performes...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2021-11-01
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| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-11-242.pdf |
| _version_ | 1818992246925557760 |
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| author | WANG Xin-ping, XIA Chun-ming, YAN Jian-jun |
| author_facet | WANG Xin-ping, XIA Chun-ming, YAN Jian-jun |
| author_sort | WANG Xin-ping, XIA Chun-ming, YAN Jian-jun |
| collection | DOAJ |
| description | Time series signals are widely used in various pattern recognition applications.In order to solve the problem of low pattern recognition rate of time series signals for a large number of targets,this article uses a variety of transform methods to convert time series signals into images,and performes pattern recognition using image classification algorithms.In the experiment,the mechanomyography (MMG) corresponding to 30 sign languages on the forearm are collected and converted into diffe-rent image styles,and a convolution neural network (CNN) framework is designed to establish pattern recognition classification models for the images.The models are optimized twice with the application of transfer learning algorithm,and the recognition rate of the best classification model reaches 98.7%,which is much higher than the recognition rate of traditional machine learning algorithms.The experimental results imply that the image processing of time series signals can effectively improve the recognition rate of multi-target pattern recognition of MMG.This paper can provide references for pattern recognition of other time series signal. |
| first_indexed | 2024-12-20T20:23:07Z |
| format | Article |
| id | doaj.art-dc7599058c5b4460a8fb20b17082c609 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-20T20:23:07Z |
| publishDate | 2021-11-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-dc7599058c5b4460a8fb20b17082c6092022-12-21T19:27:31ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2021-11-01481124224910.11896/jsjkx.201000019Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural NetworkWANG Xin-ping, XIA Chun-ming, YAN Jian-jun0School of Mechinery and Power Eningeering,East China University of Science and Technology,Shanghai 200237,ChinaTime series signals are widely used in various pattern recognition applications.In order to solve the problem of low pattern recognition rate of time series signals for a large number of targets,this article uses a variety of transform methods to convert time series signals into images,and performes pattern recognition using image classification algorithms.In the experiment,the mechanomyography (MMG) corresponding to 30 sign languages on the forearm are collected and converted into diffe-rent image styles,and a convolution neural network (CNN) framework is designed to establish pattern recognition classification models for the images.The models are optimized twice with the application of transfer learning algorithm,and the recognition rate of the best classification model reaches 98.7%,which is much higher than the recognition rate of traditional machine learning algorithms.The experimental results imply that the image processing of time series signals can effectively improve the recognition rate of multi-target pattern recognition of MMG.This paper can provide references for pattern recognition of other time series signal.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-11-242.pdfmechanomyography|pattern recognition|image-interpreted time series signal|convolution neural network|transfer learning |
| spellingShingle | WANG Xin-ping, XIA Chun-ming, YAN Jian-jun Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network Jisuanji kexue mechanomyography|pattern recognition|image-interpreted time series signal|convolution neural network|transfer learning |
| title | Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network |
| title_full | Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network |
| title_fullStr | Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network |
| title_full_unstemmed | Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network |
| title_short | Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network |
| title_sort | sign language recognition based on image interpreted mechanomyography and convolution neural network |
| topic | mechanomyography|pattern recognition|image-interpreted time series signal|convolution neural network|transfer learning |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-11-242.pdf |
| work_keys_str_mv | AT wangxinpingxiachunmingyanjianjun signlanguagerecognitionbasedonimageinterpretedmechanomyographyandconvolutionneuralnetwork |