首页|Data on Robotics and Machine Learning Reported by Triwiyanto Triwiyanto and Coll eagues (Deep learning approach to improve the recognition of hand gesture with m ulti force variation using electromyography signal from amputees)
Data on Robotics and Machine Learning Reported by Triwiyanto Triwiyanto and Coll eagues (Deep learning approach to improve the recognition of hand gesture with m ulti force variation using electromyography signal from amputees)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics and Machine L earning is the subject of a report. According to news reporting from Surabaya, I ndonesia, by NewsRx journalists, research stated, "Variations in muscular contra ction are known to significantly impact the quality of the generated EMG signal and the output decision of a proposed classifier. This is an issue when the clas sifier is further implemented in prosthetic hand design." The news correspondents obtained a quote from the research, "Therefore, this stu dy aims to develop a deep learning classifier to improve the classification of h and motion gestures and investigate the effect of force variations on their accu racy on amputees. The contribution of this study showed that the resulting deep learning architecture based on DNN (deep neural network) could recognize the six gestures and robust against different force levels (18 combinations). Additiona lly, this study recommended several channels that most contribute to the classif ier's accuracy. Also, the selected time domain features were used for a classifi er to recognize 18 combinations of EMG signal patterns (6 gestures and three for ces). The average accuracy of the proposed method (DNN) was also observed at 92. 0 ± 6.1 %. Moreover, several other classifiers were used as compari sons, such as support vector machine (SVM), decision tree (DT), K-nearest neighb ors, and Linear Discriminant Analysis (LDA). The increase in the mean accuracy o f the proposed method compared to other conventional classifiers (SVM, DT, KNN, and LDA), was 17.86 %." According to the news reporters, the research concluded: "Also, the study's impl ication stated that the proposed method should be applied to developing prosthet ic hands for amputees that recognize multi-force gestures."
SurabayaIndonesiaAsiaRobotics and Machine Learning
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