Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition

By Juan C. Núñez, R. Cabido, J. Pantrigo, A. S. Montemayor, J. Vélez. Pattern Recognition, 2018

358 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/fbe013df2014d1f71213e4816a48e697db110c91

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. Greg Van Houdt
2. Carlos Mosquera
3. Gonzalo Nápoles

Citations in the corpus

• A review on the long short-term memory model (2020, methodology): “This is skeleton-based human activity tracking (Núñez et al. 2018) where the authors used three-dimensional data sequences obtained from full-body and hand skeletons to address human activity and hand gesture recognition, respectively.”
• Bidirectional LSTM with attention mechanism and convolutional layer for text classification (2019, background): “The literature [35] proposed a deep learning-based approach for temporal 3D pose recognition problems based on a combination of a CNN and a LSTM recurrent network.”