EMG Pattern Recognition in the Era of Big Data and Deep Learning
The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2504-2289/2/3/21 |
| _version_ | 1811200883566837760 |
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| author | Angkoon Phinyomark Erik Scheme |
| author_facet | Angkoon Phinyomark Erik Scheme |
| author_sort | Angkoon Phinyomark |
| collection | DOAJ |
| description | The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on “deep learning”. Finally, directions for future research in EMG pattern recognition are outlined and discussed. |
| first_indexed | 2024-04-12T02:12:26Z |
| format | Article |
| id | doaj.art-638cb5e225a242efb029203593409572 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-04-12T02:12:26Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-638cb5e225a242efb0292035934095722022-12-22T03:52:22ZengMDPI AGBig Data and Cognitive Computing2504-22892018-08-01232110.3390/bdcc2030021bdcc2030021EMG Pattern Recognition in the Era of Big Data and Deep LearningAngkoon Phinyomark0Erik Scheme1Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, CanadaInstitute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, CanadaThe increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on “deep learning”. Finally, directions for future research in EMG pattern recognition are outlined and discussed.http://www.mdpi.com/2504-2289/2/3/21big datadeep learningelectromyogramEMGemotion recognitionfeature extractionmyoelectric controlpattern recognitionwearable sensor |
| spellingShingle | Angkoon Phinyomark Erik Scheme EMG Pattern Recognition in the Era of Big Data and Deep Learning Big Data and Cognitive Computing big data deep learning electromyogram EMG emotion recognition feature extraction myoelectric control pattern recognition wearable sensor |
| title | EMG Pattern Recognition in the Era of Big Data and Deep Learning |
| title_full | EMG Pattern Recognition in the Era of Big Data and Deep Learning |
| title_fullStr | EMG Pattern Recognition in the Era of Big Data and Deep Learning |
| title_full_unstemmed | EMG Pattern Recognition in the Era of Big Data and Deep Learning |
| title_short | EMG Pattern Recognition in the Era of Big Data and Deep Learning |
| title_sort | emg pattern recognition in the era of big data and deep learning |
| topic | big data deep learning electromyogram EMG emotion recognition feature extraction myoelectric control pattern recognition wearable sensor |
| url | http://www.mdpi.com/2504-2289/2/3/21 |
| work_keys_str_mv | AT angkoonphinyomark emgpatternrecognitionintheeraofbigdataanddeeplearning AT erikscheme emgpatternrecognitionintheeraofbigdataanddeeplearning |