首页> 外文会议>International Conference on Social Robotics >Recognition of Gestural Behaviors Expressed by Humanoid Robotic Platforms for Teaching Affect Recognition to Children with Autism - A Healthy Subjects Pilot Study
【24h】

Recognition of Gestural Behaviors Expressed by Humanoid Robotic Platforms for Teaching Affect Recognition to Children with Autism - A Healthy Subjects Pilot Study

机译:识别人形机器人平台表达的姿态行为,用于教学对自闭症儿童的认可 - 健康主题试点研究

获取原文

摘要

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is characterized by impaired social interactions and restricted, repetitive behaviors. Children who are considered to be within the spectrum tend to lack the social interaction, facial processing, and emotion recognition and implementation skills that typically developing children possess. These impairments inhibit positive social interactions, relationship building, and effective communication, which could potentially lead to distress and frustration for the child. This study focuses on developing a system to teach five of the six universal emotions. Therefore, we created an emotional gesture set to be performed on a pair of humanoid robot platforms, the NAO and the Mini Darwin. As a step towards reaching that goal of teaching and assessing children with ASD, we conducted a pilot study with able-bodied adults to validate that the gesture set created was easily recognizable. In this pilot study, we asked 137 able-bodied adult participants to watch the system perform gestures that associated to emotions. Then, we asked them to identify the emotion that the system was attempting to portray. Gestures achieved recognition rates ranging in values, with a maximum rate of 96% for sadness and a minimum rate of 57% for happiness.
机译:自闭症谱系障碍(ASD)是一种神经发育障碍,其特征是社会互动受损和受限制,重复行为。被认为是在频谱内的儿童倾向于缺乏普遍培养儿童拥有的社会互动,面部处理和情感认可和实施技巧。这些损害抑制了积极的社会互动,关系建设和有效的沟通,这可能导致孩子的痛苦和挫折。本研究侧重于开发一个系统,教授六个普遍情绪中的五个。因此,我们创建了一种在一对人形机器人平台,Nao和Mini Darwin上进行的情感手势。作为致力于达到教学和评估有ASD的儿童的一步的一步,我们通过能够拥有身体的成年人进行了一项试验研究,以验证创造的手势套装很容易识别。在这项试点研究中,我们要求137名能够拥有的成年参与者观看系统执行与情感相关的手势。然后,我们要求他们确定系统试图描绘的情绪。手势实现了价值观的识别率,最高速度为96%,悲伤和幸福的最低速度为57%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号