DocumentCode :
604667
Title :
Speed based classification of mechanomyogram using fuzzy logic
Author :
Vidhya, V.P. ; George, K.S. ; Sivanandan, K.S.
fYear :
2013
fDate :
22-23 March 2013
Firstpage :
569
Lastpage :
573
Abstract :
Mechanomyogram (MMG) signals are the mechanical signals obtained from muscles during contractions. They are less sensitive to skin impedance, sensor placement and require only low cost hardware to process the signal. Till date there are only very few applications in which MMG signals are used. The work aims at development of a standalone system for generating control signals required to drive assistive devices which provide support for disabled and elderly people. This paper presents the initial phase of the work, which focuses on the development of a fuzzy classifier. The classifier is developed to categorize the different speeds of elbow movements into rest, slow and fast. For this, MMG signal from biceps brachii are acquired and processed. Two time-domain features namely, mean absolute value and variance are extorted from the segmented data and is given to the fuzzy inference system. The average accuracy of the classifier is found to be 72.72%.
Keywords :
fuzzy logic; fuzzy reasoning; medical signal processing; signal classification; MMG signals; assistive devices; biceps brachii; disabled people; elderly people; fuzzy classifier; fuzzy inference system; mean absolute value; mechanical signals; mechanomyogram signals; sensor placement; skin impedance; speed based classification; variance; Accuracy; Feature extraction; Fuzzy logic; Fuzzy systems; Hardware; Muscles; Reactive power;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on
Conference_Location :
Kottayam
Print_ISBN :
978-1-4673-5089-1
Type :
conf
DOI :
10.1109/iMac4s.2013.6526475
Filename :
6526475
Link To Document :
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