HRI Physio Lib: A Software Framework to Support the Integration of Physiological Adaptation in HRI
| Autor: | Austin Kothig, Hamza Mahdi, John E. Muñoz, Alexander Mois Aroyo, Kerstin Dautenhahn |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Social robot
Computer science business.industry 05 social sciences 02 engineering and technology Modular design computer.software_genre Human–robot interaction Software framework Human–computer interaction 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences business Adaptation (computer science) Affective computing computer Implementation 050107 human factors |
| Zdroj: | Social Robotics ISBN: 9783030620554 ICSR |
| DOI: | 10.1007/978-3-030-62056-1_4 |
| Popis: | The rise of available physiological sensors in recent years can be attributed to the widespread popularity of commercial sensing technologies equipped with health monitoring technology. To make more engaging human-robot interaction (HRI), social robots should have some ability to infer their social partner’s affective state. Measurements of the autonomic nervous system via non-invasive physiological sensors provide a convenient window into a person’s affective state, namely their emotions, behavior, stress, and engagement. HRI research has included physiological sensors in-the-loop, however implementations are often specific to studies, and do not lend well for reusability. To address this gap, we propose a modular, flexible and extensible framework designed to work with popular robot platforms. Our framework will be compatible with both lab and consumer grade sensors, and includes essential tools and processing algorithms for affective state estimation geared towards real-time HRI applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|