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使用机器学习模型对疼痛检测的调查:叙述审查

Ruijie Fang¹, Elahe Hosseini¹, Ruoyu Zhang¹

¹Department of Electrical and Computer Engineering, University of California, Davis, CA, United States.

|February 24, 2025

概括

自动疼痛识别显示使用面部表情和生理信号的承诺. 然而,在不同的人群和环境中,在准确性方面存在挑战,因此需要进一步研究以获得可靠的临床应用.

科学领域:

生物医学工程生物医学工程
疼痛医学医学疼痛医学
人与计算机的交互

背景情况:

疼痛是一个重要的社会和临床问题,推动了客观评估方法的需求.
自动化疼痛评估技术已经进步,为临床和日常使用提供了潜在的解决方案.

研究的目的:

调查自动化疼痛识别模式及其潜在机制.
在自动化疼痛评估中确定当前的挑战和未来的研究途径.

主要方法:

进行了全面的文献审查.
对面部表情,生理信号,音频线索和瞳孔膨胀的研究进行了分析,以确定疼痛识别的有效性.

主要成果:

面部表情和生理信号显示了自动疼痛识别的巨大潜力.
模式的可靠性和准确性受到个体变化和环境因素的影响.

结论:

自动疼痛识别已经进步,但在各种环境中保持一致的准确性面临挑战.
未来的研究应该专注于提高临床整合的可靠性和适用性.

关键词:

机器学习是机器学习.手机电话手机电话手机电话疼痛疼痛疼痛疼痛疼痛评估疼痛的评估.调查调查调查调查调查调查调查调查

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