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adenovirusAnalysis of muscle fatigue conditions using time-frequency images and GLCM features 期刊名称： Current Directions in Biomedical Engineering
作者： Karthick, P.A.,Navaneethakrishna, M.,Punitha, N.,Fredo, A.R.
Jac,Ramakrishnan, S.
发光二极管英文
年份： 2016年
乘客英文版>call
期号： 第1期
关键词： gray-level co-occurrence matrix;short time fourier transform;surface electromyography;time-frequency image
摘要：In this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture reprentation of the time-frequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of the signals are considered as the non-fatigue and fati
cloyoureyesgue zones, respectively. The signals are preprocesd and the time-frequency spectrum is computed using short time fourier transform (STFT). Gray-Level Co-occurrence Matrix (GLCM) is extracted from low (15–45 Hz), medium (46–95 Hz) and high (96–150 Hz) frequency bands of the time-frequency images. Further, the features such as contrast, correlation, energy and homogeneity are calculated from the resultant matrices. The results show that nevermind
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