Three-step Attribute Selection for Stress Detection based on Physiological Signals

Abstract

We present a three-step method for attribute selection that builds on person-independent and person-specific feature assessment stages. The first two steps aim to select a person-independent subset of attributes that are repeatedly selected for a large population of users. Next, this selection is intersect with a person-specific subset derived from the Fisher's separation criterion. As a result, we obtain a subset of attributes which is both task-specific and customized to the quality of data of each particular user. The proposed method was validated on the ASCERTAIN database in an experimental setup oriented towards high-arousal negative-valence detection based on physiological signals. The experimental results support that the proposed method offers advantage in terms of detection accuracy when compared to other subset selection strategies.