Recurrent Neural Networks with Weighting Loss for Early Prediction of Hand Movements


We propose in this work an approach for earlyprediction of hand movements using recurrent neural networks(RNNs) and a novel weighting loss. The proposed loss functionleverages the outputs of an RNN at different time steps andweights their contributions to the final loss linearly over timesteps. Additionally, a sample weighting scheme also constitutesa part of the weighting loss to deal with the scarcity of thesamples where a change of hand movements takes place. Theexperiments conducted with the Ninapro database reveal thatour proposed approach not only improves the performance inthe early prediction task but also obtains state of the art resultsin classification of hand movements. These results are especiallypromising for the amputees.
Original languageEnglish
Number of pages5
Publication statusPublished - 01.09.2018
Event26th European Signal Processing Conference - Rome, Italy
Duration: 03.09.201807.09.2018


Conference26th European Signal Processing Conference
Abbreviated titleEUSIPCO 2018
Internet address


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