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

•Combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network for skeleton-based human activity and hand gesture recognition.•Two-stage training strategy which firstly focuses on the CNN training and, secondly, adjusts the full method CNN+LSTM.•A method for data augmentation in the context of spatiotemporal 3D data sequences.•An exhaustive experimental study on publicly available data benchmarks with respect to the state-of-the-art most representative methods.•Comparison among different CPU and GPU platforms.