A Random Subspace Based Approach for Stress Classification from Smartphone Data

Stress is an important problem to deal with in today’s society. Thanks to the built-in sensors of smartphones, many operations can be performed unobtrusively. Accordingly, smartphones are among the indispensable data sources of research subjects. In this study, the problem of stress classification was discussed with the data obtained from smartphones. Sensor data were collected to examine users’ writing behavior. The features extracted from the obtained raw data were classified with the random subspace-based structures and their performances were compared. As a result of the study, it was observed that the stacking method showed promising results.