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• W2765609408 abstract "Surface electromyogram is the best method to record muscle activity. It causes minimum risk to the subject and is non-invasive in nature. SEMG signals are used for rehabilitation engineering and varied prosthetic technology based applications. Now a day's, SEMG signals are used to develop various controlling prototype based on gesture recognition modules. These modules recognize different classified movements and use them to control any machine. This paper proposed a fuzzy logic technique for classifying dorsal flexion, extension and no movement of foot using fuzzy approach. Surface electromyography signals (SEMG) were extracted using hardware consisting of differential amplifier, noninverting amplifier, peak filter and interface module. SEMG signals were acquired from Gastrocnemius literalis muscle responsible for these movements. Matlab softscope was used to import signals from hardware to system. Fuzzy logic controller was used to classify foot movements. For better classification, three parameters; Root Mean Square, Median and Standard Deviation were selected as inputs to fuzzy logic controller. Results proved standard deviation was to be the best parameter for discriminating movements." @default.
• W2765609408 created "2017-11-10" @default.
• W2765609408 creator A5027833891 @default.
• W2765609408 creator A5037103400 @default.
• W2765609408 date "2016-11-01" @default.
• W2765609408 modified "2023-09-27" @default.
• W2765609408 title "Classification of foot movements using fuzzy logic techniques" @default.
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• W2765609408 doi "https://doi.org/10.1109/iicpe.2016.8079351" @default.
• W2765609408 hasPublicationYear "2016" @default.
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