ArticleInternational journal of social robotics2023
Experience with an Affective Robot Assistant for Children with Hearing Disabilities.
Article in International journal of social robotics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
- The Roles of Autonomic Arousal and Self-Reported Stress in Children's Disclosure of a Minor Transgression.Annals of the New York Academy of Sciences · 2026Article
- Care provided by humanoid robots: a scoping review.Investigacion y educacion en enfermeria · 2025Article
- CARE: towards customized assistive robot-based education.Frontiers in robotics and AI · 2025Article
- Biosignal-integrated robotic systems with emerging trends in visual interfaces: A systematic review.Biophysics reviews · 2024Review
- Evaluating speech-in-speech perception via a humanoid robot.Frontiers in neuroscience · 2024Article
- A physiological signal database of children with different special needs for stress recognition.Scientific data · 2023Article
- Article
- Information Security in Medical Robotics: A Survey on the Level of Training, Awareness and Use of the Physiotherapist.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents an assistive robotic system enhanced with emotion recognition capabilities for children with hearing disabilities. The system is designed and developed for the audiometry tests and rehabilitation of children in a clinical setting and includes a social humanoid robot (Pepper), an interactive interface, gamified audiometry tests, sensory setup and a machine/deep learning based emotion recognition module. Three scenarios involving conventional setup, tablet setup and setup with the robot+tablet are evaluated with 16 children having cochlear implant or hearing aid. Several machine learning techniques and deep learning models are used for the classification of the three test setups and for the classification of the emotions (pleasant, neutral, unpleasant) of children using the recorded physiological signals by E4 wristband. The results show that the collected signals during the tests can be separated successfully and the positive and negative emotions of children can be better distinguished when they interact with the robot than in the other two setups. In addition, the children's objective and subjective evaluations as well as their impressions about the robot and its emotional behaviors are analyzed and discussed extensively.
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Registered trials
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.