首页|Data from Escuela Politecnica Nacional Provide New Insights into Intelligent Sys tems (CNN-LSTM and post-processing for EMGbased hand gesture recognition)
Data from Escuela Politecnica Nacional Provide New Insights into Intelligent Sys tems (CNN-LSTM and post-processing for EMGbased hand gesture recognition)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on intelligent s ystems have been published. According to news reporting from Quito, Ecuador, by NewsRx journalists, research stated, “Hand Gesture Recognition (HGR) using elect romyography (EMG) signals is a challenging problem due to the variability and no ise in the signals across individuals.” Financial supporters for this research include Escuela Politecnica Nacional. The news journalists obtained a quote from the research from Escuela Politecnica Nacional: “This study addresses this challenge by examining the effect of incor porating a post-processing algorithm, which filters the sequence of predictions and removes spurious labels, on the performance of a HGR model based on spectrog rams and Convolutional Neural Networks (CNN). The study also compares CNN vs CNN -LSTM to assess the influence of the memory cells on the model. The EMG-EPN-612 dataset, which contains measurements of EMG signals for 5 hand gestures from 612 subjects, was used for training and testing. The results showed that the post-p rocessing algorithm increased the recognition accuracy by 41.86% f or the CNN model and 24.77% for the CNN-LSTM model. The inclusion of the memory cells increased accuracy by 3.29%, but at the cost of 53 times more learnables. The CNN-LSTM model with post-processing achieved a me an recognition accuracy of 90.55% (SD=9.45%).”
Escuela Politecnica NacionalQuitoEcu adorSouth AmericaEmerging TechnologiesGesture RecognitionIntelligent Sys temsMachine Learning
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