Abstract
Recognition of hand gestures has been developed in various research domains and proven to have significant benefits in improving the necessity of human-robot interaction (HRI). The introduction of intelligent statistics knowledge methodologies, such as big data and machine learning, has ushered in a new era of data science and made it easier to classify hand motions accurately using electromyography (EMG) signals. However, the collecting and labelling of the vast dataset enforces a significant workload; resulting in implementations takes a long time. As a result, a unique strategy for combining the advantages of depth vision learning with EMG-based hand gesture detection was developed. It is accomplished of automatically categorizing the class of the obtained EMG data using ensemble learning without considering the hand motion sequence. The models were built and interpreted using the SVM with RBF kernel, Random Forest, and Catboost with the best hyperparameters. The resultant value states that Catboost produces the best accuracy of around 0.95 as compared with other models. This demonstrates that the suggested technique can recognize hand gestures with better performance rate.
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