Abstract: Traditional sensors are prone to receive the interference of external environmental factors due to achieving human-machine interaction in specific spatial area. 3D handwriting recognition of smartphones based on the long short-term memory (LSTM) neural network is proposed, which can be used in human-machine interaction in non-specific 3D spaces. First, three-axis acceleration sensors of smartphones are used to collect data which perform pre-processing operations to construct a 3D handwriting recognition dataset. Then, the 3D handwriting recognition model based on LSTMis constructed and pre-trained by adopting the constructed datasets. Finally, the trained model is applied to implement 3D handwriting classification recognition for smartphones. By testing on a self-built non-dependent user dataset, experimental results show that the proposed model can achieve the accuracy rate of 86.4%, recall rate of 88.1%, precision rate of 88.4%, and F₁ score of 88.0%.

Key words: smartphone, acceleration sensor, 3D handwriting recognition, LSTM