How An Automated Gesture Imitation Game Can Improve Social Interactions With Teenagers With ASD

• URL: http://arxiv.org/abs/2007.05394v1
• Date: Fri, 10 Jul 2020 14:01:24 GMT
• Title: How An Automated Gesture Imitation Game Can Improve Social Interactions With Teenagers With ASD
• Authors: Linda Nanan Vall\'ee (ESATIC), Sao Mai Nguyen (IMT Atlantique, IMT Atlantique - INFO, Lab-STICC, Flowers), Christophe Lohr (Lab-STICC, IMT Atlantique - INFO, IMT Atlantique), Ioannis Kanellos (Lab-STICC, IMT Atlantique - INFO, IMT Atlantique), Olivier Asseu (ESATIC)
• Abstract summary: We present an interaction scenario adapted to ASD teenagers. We propose a computational architecture using the latest machine learning algorithm Openpose for human pose detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the outlook of improving communication and social abilities of people with ASD, we propose to extend the paradigm of robot-based imitation games to ASD teenagers. In this paper, we present an interaction scenario adapted to ASD teenagers, propose a computational architecture using the latest machine learning algorithm Openpose for human pose detection, and present the results of our basic testing of the scenario with human caregivers. These results are preliminary due to the number of session (1) and participants (4). They include a technical assessment of the performance of Openpose, as well as a preliminary user study to confirm our game scenario could elicit the expected response from subjects.

Related papers

• Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder [6.197934754799159]
  This paper proposes an end-to-end framework for automatically predicting the social communication severity of children with ASD from raw speech data. Achieving a Pearson Correlation Coefficient of 0.6566 with human-rated scores, the proposed method showcases its potential as an accessible and objective tool for the assessment of ASD.
  arXiv  Detail & Related papers  (2024-08-30T14:43:58Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
  This work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior. Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents.
  arXiv  Detail & Related papers  (2022-08-22T14:06:06Z)
• Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation [0.0]
  We present BBSI, the first set of annotations of complex Bodily Behaviors embedded in continuous Social Interactions. Based on previous work in psychology, we manually annotated 26 hours of spontaneous human behavior. We adapt the Pyramid Dilated Attention Network (PDAN), a state-of-the-art approach for human action detection.
  arXiv  Detail & Related papers  (2022-07-26T11:24:00Z)
• An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition [98.70304981174748]
  We focus on the general applications of pretrained speech representations, on advanced end-to-end automatic speech recognition (E2E-ASR) models. We select several pretrained speech representations and present the experimental results on various open-source and publicly available corpora for E2E-ASR.
  arXiv  Detail & Related papers  (2021-10-09T15:06:09Z)
• Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
  This study focuses on providing a novel learning mechanism based on a rivalry social impact. Based on the concept of competitive rivalry, our analysis aims to investigate if we can change the assessment of these agents from a human perspective.
  arXiv  Detail & Related papers  (2020-11-02T21:54:18Z)
• Building an Automated Gesture Imitation Game for Teenagers with ASD [0.0]
  Autism spectrum disorder is a neurodevelopmental condition that includes issues with communication and social interactions. People with ASD also often have restricted interests and repetitive behaviors. In this paper we build preliminary bricks of an automated gesture imitation game that will aim at improving social interactions with teenagers with ASD.
  arXiv  Detail & Related papers  (2020-07-09T07:27:43Z)
• The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents [54.63186041942257]
  We propose a virtual simulation environment that implements the Chef's Hat card game, designed to be used in Human-Robot Interaction scenarios. This paper provides a controllable and reproducible scenario for reinforcement-learning algorithms.
  arXiv  Detail & Related papers  (2020-03-12T15:52:49Z)
• Modeling Engagement in Long-Term, In-Home Socially Assistive Robot Interventions for Children with Autism Spectrum Disorders [5.699538935722362]
  This work applies supervised machine learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. We present two types of engagement models for each user: (i) generalized models trained on data from different users; and (ii) individualized models trained on an early subset of the user's data. Results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings.
  arXiv  Detail & Related papers  (2020-02-06T18:26:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.