Recording affect in the field: Towards methods and metrics for improving ground truth labels

Abstract

One of the primary goals of affective computing is enabling computers to recognize human emotion. To do this we need accurately labeled affective data. This is challenging to obtain in real situations where affective events are not scripted and occur simultaneously with other activities and feelings. Affective labels also rely heavily on subject self-report for which can be problematic. This paper reports on methods for obtaining high quality emotion labels with reduced bias and variance and also shows that better training sets for machine learning algorithms can be created by combining multiple sources of evidence. During a 7 day, 13 participant field study we found that recognition accuracy for physiological activation improved from 63% to 79% with two sources of evidence and in an additional pilot study this improved to 100% accuracy for one subject over 10 days when context evidence was also included. © 2011 Springer-Verlag.