EMG-based Finger Movement Classification using transparent Fuzzy System

Myoelectric signal (MES) is the electrical manifestation of muscular contraction. The transient part of the EMG signal, which is recorded at the surface of the skin of the forearm, has been exploited to provide the recognition of three finger movements. The objective of the paper is to describe the identification procedure, based on Transparent (interpretability) Fuzzy System and accuracy, which allow the cooperation between expert rules and induced rules. An initial fuzzy rule system is generated using the statistic’s trimmed mean method [7], rather clustering, which fulfils many criteria for transparency and semantic. Redundant sets (similar fuzzy sets) are removed based on a similarity measure [15,11]. The tuning of premise and consequents of zero-order Takagi Sogeno [14] fuzzy model is obtained with Gradient descent and Least squares Estimator respectively in a combined hybrid algorithm. The obtained results are compared to the subtractive clustering [13] method. The presented method may be used for real time applications control regarding to its low computation costs and transparency.