HAR-stacked-residual-bidir-LSTMs
Activity recognizer
An implementation of a deep neural network architecture for Human Activity Recognition using stacked residual bidirectional LSTM cells with TensorFlow.
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
319 stars
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100 forks
Language: Python
last commit: almost 4 years agoLinked from 1 awesome list
bidirectional-lstm-cellshuman-activity-recognitionlstmresidual-layersresidual-lstm-cellsrnnstacked-layerstensorflow
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