Fuzzy Logic for Grasping Type Classification of Human Hand Based on Myoelectric Signal Parameter

Abstract

This research aim to classify grasping types between power grip and precision grip based on myoelectric signal parameters. Ten healthy male with normal grasping strengths, and ideal body mass index (BMI) were choosen as subject and asked to perform several grasping types, i.e. spherical, cylindrical, lateral, hook and tip. Myoelectric signal were captured by AD620 based circuits from subjects’ lower arms, both in fresh and fatigue condition and output were processed by using TRUE RTA (Real Time Analyzer) software to get their frequency based form. After that, there were calculations to determine their frequency base parameter of  Mean Frequency (MNP), Median Frequency (MDF), Mean Power (MNP), Total Power (TTP) and Spectral Moment (SM). Based on our researh MNF, MDF, TTP, MNP and SM are found to be parameters with high robustness caracteristic which can be proposed as a fuzzy logic control input parameter. Based on our trial, this logic control model can already distinguish power grip and precission grip within the input range.