Automated affect classification and task difficulty adaptation in a competitive scenario based on physiological linkage: An exploratory study. Issue 153 (September 2021)

• Record Type: Journal Article
• Authors: Darzi, Ali; Novak, Domen
• Is Part Of: International journal of human-computer studies. Issue 153(2021)
• Publication Date: 2021-09
• Subjects: Affective computing -- Competition -- Physiological measurements -- Physiological linkage -- Pattern recognition -- Dynamic difficulty adaptation; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2021.102673
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: In multi-user scenarios, difficulty generally adapted based on performance. Could alternatively be adapted based on physiological measurements of both users. Automated classification of human psychological states in competitive scenario. Based on individual physiological responses and physiological linkage. Task difficulty then dynamically adapted based on classified human states. 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).