大葉大學圖書館 |

EMG signals characterization in three states of contraction by fuzzy network and feature extraction[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	621.3822
書名/作者:	EMG signals characterization in three states of contraction by fuzzy network and feature extraction/ by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan.
作者:	Mokhlesabadifarahani, Bita.
其他作者:	Gunjan, Vinit Kumar.
出版者:	Singapore : : Springer Singapore :, 2015.
面頁冊數:	xv, 35 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Signal processing - Digital techniques.
標題:	Fuzzy systems.
標題:	Engineering.
標題:	Biomedical Engineering.
標題:	Orthopedics.
標題:	Forensic Science.
標題:	Computational Biology/Bioinformatics.
標題:	Health Informatics.
標題:	Rehabilitation.
ISBN:	9789812873200 (electronic bk.)
ISBN:	9789812873194 (paper)
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、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-981-287-320-0

EMG signals characterization in three states of contraction by fuzzy network and feature extraction[electronic resource] /
Mokhlesabadifarahani, Bita.

EMG signals characterization in three states of contraction by fuzzy network and feature extraction[electronic resource] /by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan. - Singapore :Springer Singapore :2015. - xv, 35 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology, Forensic and medical bioinformatics,2191-530X. - SpringerBriefs in applied sciences and technology.Forensic and medical bioinformatics..

Introduction to EMG Technique and Feature Extraction -- Methodology for working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.

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.

ISBN: 9789812873200 (electronic bk.)

Standard No.: 10.1007/978-981-287-320-0doiSubjects--Topical Terms:

405038
Signal processing
--Digital techniques.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

EMG signals characterization in three states of contraction by fuzzy network and feature extraction[electronic resource] /
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