  Call and messaging logs from mobile devices have been used to predict human
personality traits successfully in recent years. However, the widely available
accelerometer data is not yet utilized for this purpose. In this research, we
explored some important features describing human physical activity intensity,
used for the very first time to predict human personality traits through raw
accelerometer data. Using a set of newly introduced metrics, we combined
physical activity intensity features with traditional phone activity features
for personality prediction. The experiment results show that the predicted
personality scores are closer to the ground truth, with observable reduction of
errors in predicting the Big-5 personality traits across male and female.
