用面部表情来识别中风后:一项试点研究
Guilherme C Oliveira1, Quoc C Ngo2, Leandro A Passos3
1School of Sciences, São Paulo State University, São Paulo, Brazil; School of Engineering, Royal Melbourne Institute of Technology, Victoria, Australia.
Computer methods and programs in biomedicine
|May 1, 2024
概括
面部表情的计算机分析可以帮助救护人员早期发现中风症状. 这种方法通过分析微妙的口腔肌肉运动,准确地识别中风后 (PS) 个体,从而有可能改善治疗的及时性.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 神经学 神经学
背景情况:
- 早期识别中风症状对于及时治疗和改善患者结果至关重要.
- 护理人员经常错过中风病例,因为在初始症状识别方面存在挑战.
- 面部表情代表了最早可观察到的中风指标之一.
研究的目的:
- 开发和评估一种计算机化方法来分析面部表情,以区分中风后 (PS) 个体和健康对照 (HC).
- 评估使用行动单元用于客观和自动检测中风症状的潜力.
主要方法:
- 利用来自多伦多神经面部数据集的RGB视频,展示了14个PS和11个HC个体.
- 在面部检查期间使用行动单元来分析特定的面部运动.
- 使用XGBoost进行计算的动作单元,并执行面部表情分类的回归分析,无需人工干预.
主要成果:
- 在"亲吻和传播"面部表情分析中获得了82%的准确性.
- 在区分PS和HC时,表现出91%的高灵敏度.
- 确定了与口腔肌肉相关的特征是最有效的中风检测.
结论:
- 这项试点研究表明,自动化面部表情分析可以检测中风后的指标.
- 需要在现实环境中进行进一步的验证,跨越多元种族和不同智能手机使用情况.
- 拟议的方法显示了智能手机为基础的查的前响应者,以促进迅速的中风治疗启动的承诺.
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