ArticleInternational journal of human-computer studies2021
Automated affect classification and task difficulty adaptation in a competitive scenario based on physiological linkage: An exploratory study.
Article in International journal of human-computer studies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 30 citations in OpenAlex.
- Automated classification of conversation valence and arousal using autonomic nervous system responses.Frontiers in neuroscience · 2026Article
- TruVox Web-Based Software for Vocal Pitch Training in Transgender Women: Development and Single-Session Evaluations.JMIR formative research · 2025Article
- Estimating the valence and arousal of dyadic conversations using autonomic nervous system responses and regression algorithms.Frontiers in neuroergonomics · 2025Article
- Regression of Multiple Conversation Aspects using Dyadic Physiological Measurements.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Implementing a student-centered stroke intervention and prevention education program; evaluating motivation, cognitive load, and performance among middle school students.Frontiers in public health · 2024Article
- Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements.Frontiers in neuroscience · 2021Article
- Brain Synchrony in Competition and Collaboration During Multiuser Neurofeedback-Based Gaming.Frontiers in neuroergonomics · 2021Article
- Motor Learning in Robot-Based Haptic Dyads: A Review.IEEE transactions on hapticsReview
- Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses.IEEE transactions on affective computingArticle
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.