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).

在竞争和合作的场景中，任务难度应该动态调整以适应不同能力的人。这种场景的最先进的难度适应方法是基于任务性能的，它传达的关于用户特定因素（如工作量）的信息很少。因此，我们对基于生理联系（参与者生理反应的协变）的竞争场景中的自动情感识别和任务难度适应进行了探索性研究。分类算法是在一项开环研究中开发的，其中 16 对玩了一场竞技游戏，同时测量了 5 种生理反应：呼吸、皮肤电导、心电图和 2 种面部肌电图。生理和表现数据用于对四个自我报告的变量（享受、效价、唤醒，感知困难）分为两到三类。对于感知难度，获得了最高的分类准确率：二类分类为 84.3%，三类分类为 60.5%。作为概念验证，开发的分类器用于小型闭环研究以动态调整游戏难度。虽然这项闭环研究没有发现基于生理适应的明显优势，但它证明了这种实时适应的技术可行性。从长远来看，基于生理学的任务适应可以增强许多多用户环境（例如，教育、制造、锻炼）中的竞争与合作。作为概念验证，开发的分类器用于小型闭环研究以动态调整游戏难度。虽然这项闭环研究没有发现基于生理适应的明显优势，但它证明了这种实时适应的技术可行性。从长远来看，基于生理学的任务适应可以增强许多多用户环境（例如，教育、制造、锻炼）中的竞争与合作。作为概念验证，开发的分类器用于小型闭环研究以动态调整游戏难度。虽然这项闭环研究没有发现基于生理适应的明显优势，但它证明了这种实时适应的技术可行性。从长远来看，基于生理学的任务适应可以增强许多多用户环境（例如，教育、制造、锻炼）中的竞争与合作。