Self Organizing Maps for Affective State Detection

In this contribution we present two experimental scenarios in which we employed Self Organizing Maps (SOMs) to detect affective states. The first scenario is related towards designing a “Personal Stress Prevention Assistant”: we summarize our efforts to detect affective information related to stress in the posture channel. We show that a person-independent discrimination of stress from cognitive load is feasible when using data from a pressure mat mounted on a seat. The second scenario is embedded towards assisting patients with manic depression: we present preliminary results in detecting emotions from voice data. Our findings illustrate that a person-dependent discrimination of emotions from voice data seems feasible and that a general model might be appropriate to discriminate high and low levels of arousal.