User identification using deep learning and human activity mobile sensor data

The ownership of user actions in computer and mobile applications is an important concern, especially when using shared devices. User identification using physical biometric authentication methods permits the actual user to access the device. However, in cases where different users may access a shared device during the same active session, the person who owns the active session will be accountable for any actions performed by the other users. Thus, user identification using behavioral characteristics has come into the picture. Human activity recognition from mobile sensor data is gaining more interest with the advent of mobile devices and the emergence of the Internet of Things, where different applications such as elderly health monitoring, athletic evaluation, and context-aware behavior are being developed. In this paper, we show how human activity data can be utilized to identify the actual device user. We build deep learning models that are capable of identifying the users of mobile and wearable devices based on their body movements and daily activities. We use the Long Short-Term Memory classifier for building the user identification model based on time-series data from mobile motion sensors. The model targets the users that were involved in the training process. We tested our approach on two publicly available human activity datasets that contain daily activities and fall states data from accelerometer and gyroscope mobile sensors. The results show that the models are capable of identifying the actual mobile device users from their motion data with an accuracy of up to 90%. Further, the results show that the model from the accelerometer data outperforms the one from gyroscope data.