Automated affect classification and task difficulty adaptation in a competitive scenario based on physiological linkage: An exploratory study

Abstract

In competitive and cooperative scenarios, task difficulty should be dynamically adapted to suit people with different abilities. State-of-the-art difficulty adaptation methods for such scenarios are based on task performance, which conveys little information about user-specific factors such as workload. Thus, we present an exploratory study of automated affect recognition and task difficulty adaptation in a competitive scenario based on physiological linkage (covariation of participants’ physiological responses). Classification algorithms were developed in an open-loop study where 16 pairs played a competitive game while 5 physiological responses were measured: respiration, skin conductance, electrocardiogram, and 2 facial electromyograms. Physiological and performance data were used to classify four self-reported variables (enjoyment, valence, arousal, perceived difficulty) into two or three classes. The highest classification accuracies were obtained for perceived difficulty: 84.3% for two-class and 60.5% for three-class classification. As a proof of concept, the developed classifiers were used in a small closed-loop study to dynamically adapt game difficulty. While this closed-loop study found no clear advantages of physiology-based adaptation, it demonstrated the technical feasibility of such real-time adaptation. In the long term, physiology-based task adaptation could enhance competition and cooperation in many multi-user settings (e.g., education, manufacturing, exercise).