A STGCN-GRU-BiLSTM model detects the frame of ground impact in falls from 3D skeleton data, reaching a reported 97.5% accuracy on a relabeled UP-Fall subset.
´A., Barros, L.M.: Impact of educational intervention in the perception of hospitalised patients about the risk of falling and associated factors
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Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data
A STGCN-GRU-BiLSTM model detects the frame of ground impact in falls from 3D skeleton data, reaching a reported 97.5% accuracy on a relabeled UP-Fall subset.