Gait identification and authentication using LSTM based on 3-axis accelerations of smartphone

In this study, we apply recurrent neural networks and Long Short-Term Memory (LSTM) to 3-axis accelerations of walking acquired by a smartphone for gait identification and authentication. First, the accelerations during walking for 21 subjects are recorded in two holding situations, namely, while placing the smartphone in the pocket or looking at the screen of the smartphone. We then identify and authenticate the user by inputting the accelerations directly to the recurrent neural networks and LSTM. We have confirmed that the performance of this method is better than the conventional method of extracting features from the accelerations and using random forests.