Volume - 4 | Issue - 3 | september 2022
DOI
10.36548/jei.2022.3.007
Published
14 September, 2022
In this paper, different machine learning and tabular learning classification algorithms have been studied and compared on the acute hand-gesture Electromyogram dataset. The comparative study between different models such as KNN, RandomForest, TabNet, etc. depicts that small datasets can achieve high-level accuracy along with the intuition of high-performing neural net architectures through tabular learning approaches like TabNet. The performed analysis produced an accuracy of 99.9% through TabNet while other conventional classifiers also gave satisfactory results with KNN being at highest achieving accuracy of 97.8 %.
KeywordsMachine Learning TabNet Hand Gestures EMG Dataset XG Boost Algorithm