SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features

In the recent era of technology, biomedical signals have been attracted lots attention regarding development rehabilitation robotic technology. The surface electromyography (SEMG) are fabulous utilized in field robotics. this context, SEMG acquired by twenty-five right-hand dominated healthy human subjects to discriminate various hand gestures. placement electrodes has done according predefined acupressure point required movements. After signal acquisition, pre-processing and noise rejection performed. de-noising four levels decomposition accomplished discrete wavelet transform (DWT). article, third fourth-level detail coefficients for time-scale feature extractions. performance ten features evaluated compared each other with three-fold cross-validation technique using a Decision Tree (DT) Linear Regression (LR) classifier. results demonstrated that DT classifier classification accuracy was found superior LR By 96.3% achieved, all combined as vector.