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使用NAO学习手语:人形机器人作为帮助学习哥伦比亚手语的工具

Juan E Mora-Zarate¹, Claudia L Garzón-Castro¹, Jorge A Castellanos Rivillas²

¹Engineering Faculty, Research Group CAPSAB, Universidad de La Sabana, Chía, Colombia.

Frontiers in robotics and AI

|November 29, 2024

概括

本研究评估了使用机器学习和人形机器人对哥伦比亚手语 (CSL) 颜色的新教育工具. 该系统实现了高精度,为CSL教育提供了有前途的解决方案.

科学领域:

康复和辅助技术康复和辅助技术
人与计算机的互动.
计算语言学计算语言学

背景情况:

手语对于听力损失康复至关重要,但区域差异和口译员短缺阻碍了教育.
哥伦比亚手语 (CSL) 教育面临的挑战是由于口译员的可用性有限.
机器学习和计算机视觉为手语翻译和教育提供了潜在的解决方案.

研究的目的:

评估一项旨在支持学习哥伦比亚手语 (CSL) 11种基于颜色的标志的新活动的表现.
评估利用MediaPipe和神经网络进行CSL彩色标志识别的系统的有效性.
探索NAO人形机器人的集成,以加强CSL教育中的非语言交互.

主要方法:

开发了一种具有两种模式的交互式评估方法:用户选择和随机标志评估.
使用MediaPipe提取骨干和手部坐标以获取神经网络输入.
使用网络摄像头和NAO H25 V6机器人摄像头 (无线流) 评估神经网络性能.

主要成果:

该系统使用网络摄像头输入实现了91.6%的F1得分,预测时间为85.2毫秒.
使用NAO机器人的摄像头,该系统获得了93.8%的F1得分和2.29秒的预测时间.
利用NAO机器人的25度自由度进行非语言的人机交互.

结论:

拟议的系统在识别哥伦比亚手语彩色标志方面表现出高准确性和效率.
与NAO等类人机器人集成为聋人社区提供了更丰富,更互动的教育体验的潜力.
这项工作为开发哥伦比亚手语教育的先进辅助技术提供了基础.

关键词:

哥伦比亚手语 (CSL) 是一种哥伦比亚手语.教育教育教育教育的教育.人与机器人的交互 (HRI)机器学习是机器学习.神经网络的神经网络的神经网络社交机器人社交机器人

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