Analysis of Chronic Joint Pain using Soft Computing Techniques with Feature Extraction and Classification based on sEMG Signals

Electronics Engineering

Joint pain is the one of the major health related problem reported by many numbers of patients. Accurate measurement of pain is not ever having universal standard reference unit of measure and overall subject’s pain information solely depends on the survey form only. A study of finding the correlation between EMG signal and pain is the prime moto of current research work. Chronic joint pain especially in the area located at knee and ankle is a severe health concern today. Therefore, multiple clinical remedies have been suggested to get rid of these health problems. Level of pain measurement is a survey type questionnaire on ten-point scaling. Electroencephalogram (EEG) signal is best correlated with the physiological symptoms and overall pain related information, which is global in nature. Support Vector Machine on processed sEMG data is used for the classification of pain in three different categories like Normal, Moderate and Severe. Performance analysis of present method is validated with the survey pain diagnostic feedback forms

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