ArticleScientific data2024
Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A large scale dataset of emotion annotated physiological signals collected from a public exhibit.Scientific reports · 2026Article
- A Multimodal Dataset for Assessing Emotion, Stress, and Emotional Workload in Interpersonal Work Scenario.Scientific data · 2026Article
- Lightweight Convolutional Neural Network with Efficient Channel Attention Mechanism for Real-Time Facial Emotion Recognition in Embedded Systems.Sensors (Basel, Switzerland) · 2025Article
- Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set.Scientific data · 2024Article
- A roadmap for improving data quality through standards for collaborative intelligence in human-robot applications.Frontiers in robotics and AI · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In human-human and human-robot interaction, the counterpart influences the human's affective state. Contrary to humans, robots inherently cannot respond empathically, meaning non-beneficial affective reactions cannot be mitigated. Thus, to create a responsible and empathetic human-robot interaction (HRI), involving anthropomorphic service robots, the effect of robot behavior on human affect in HRI must be understood. To contribute to this understanding, we provide the new comprehensive data set AFFECT-HRI, including, for the first time, physiological data labeled with human affect (i.e., emotions and mood) gathered from a conducted HRI study. Within the study, 146 participants interacted with an anthropomorphic service robot in a realistic and complex retail scenario. The participants' questionnaire ratings regarding affect, demographics, and socio-technical ratings are provided in the data set. Five different conditions (i.e., neutral, transparency, liability, moral, and immoral) were considered during the study, eliciting different affective reactions and allowing interdisciplinary investigations (e.g., computer science, law, and psychology). Each condition includes three scenes: a consultation regarding products, a request for sensitive personal information, and a handover.
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Registered trials
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