为严重残疾人创建基于图像的AI增强的莫尔斯代码翻译系统
Chung-Min Wu1, Yeou-Jiunn Chen2, Shih-Chung Chen2
1Department of Intelligent Automation Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan.
Bioengineering (Basel, Switzerland)
|November 25, 2023
概括
这项研究引入了一个使用面部运动进行交流的AI驱动系统,使严重身体障碍的人能够控制设备并表达需求,提高独立性和生活质量.
科学领域:
- 辅助技术 辅助技术 辅助技术
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 患有严重身体障碍的患者 (脊髓损伤,脑,肌缩侧面硬化症) 经历了显著的移动性和沟通挑战.
- 移动性有限会导致自我照顾能力的丧失和日常沟通的严重困难.
- 严重的病例可能涉及言语丧失,进一步孤立个人.
研究的目的:
- 设计和开发基于人工智能的图像辅助通信系统,用于严重身体障碍的人.
- 使用户能够通过面部运动来沟通日常需求,并通过面部运动控制环境外围设备.
- 提高移动和沟通能力有限的用户的生活质量和独立性.
主要方法:
- 利用人工智能进行精确的面部定位和运动识别.
- 从识别的面部动作中生成摩尔斯号码.
- 将面部生成的摩尔斯代码翻译成可读的字符或用于计算机控制和通信的命令.
- 采用模糊算法进行数据识别和分析.
主要成果:
- 该系统在23个人类类型数据集上使用模糊算法实现了高识别率.
- 专家生成的数据的平均识别率为99.83%,残疾人的数据为98.6%.
- 通过面部表情成功控制计算机软件和环境外围设备.
结论:
- 开发的系统使严重身体障碍的用户能够通过面部运动来表达需求和想法.
- 基于面部的沟通显著改善了用户的生活质量,并促进了独立的生活.
- 无触摸操作增强了方便和安全性,为辅助通信提供了实用的解决方案.
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