EMG signals characterization in three states of contraction by fuzzy network and feature extraction [electronic resource]
- 作者： Mokhlesabadifarahani, Bita.
- 其他作者：
- 其他題名：
- SpringerBriefs in applied sciences and technology.
- 出版： Singapore : Springer Singapore :Imprint: Springer
- 叢書名： SpringerBriefs in applied sciences and technology, Forensic and medical bioinformatics,
- 主題： Signal processing--Digital techniques , Fuzzy systems , Engineering , Biomedical engineering , Orthopedics , Forensic Science. , Computational Biology/Bioinformatics. , Health Informatics. , Rehabilitation
- ISBN： 9789812873200 (electronic bk.) 、 9789812873194 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Introduction to EMG Technique and Feature Extraction -- Methodology for working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.
- 摘要註: Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.
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讀者標籤：
- 系統號： 005128121 | 機讀編目格式
館藏資訊
Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.