Predicting Personality Traits from Physical Activity Intensity
This work addresses personality prediction for mobile users, but it is incremental as it builds on existing phone activity methods by adding new data.
The paper tackled predicting Big-5 personality traits by using physical activity intensity features from accelerometer data for the first time, resulting in observable error reductions and predicted scores closer to ground truth across genders.
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.