从面部表情到算法:对动物疼痛识别技术的叙述性审查
Ludovica Chiavaccini1, Anjali Gupta1, Guido Chiavaccini2
1Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States.
Frontiers in veterinary science
|August 1, 2024
概括
使用人工智能的自动疼痛识别 (APR) 提供了客观的动物疼痛评估,克服了像马笑容尺度 (HGS) 这样的依赖人类的尺度的局限性. 这项技术分析面部表情和其他信号,以改善动物福利.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 动物福利科学 动物福利科学
- 医疗保健中的人工智能
背景情况:
- 面部表情对于动物的沟通和情绪状态至关重要.
- 现有的疼痛量表,如马笑容量表 (HGS) 是主观的,需要广泛的训练.
- 目前方法的局限性需要对动物提供更客观的疼痛评估工具.
研究的目的:
- 为动物提供自动疼痛识别 (APR) 的全面概述.
- 追踪从面部表情疼痛尺度到人工智能驱动的APR的演变.
- 讨论APR在动物福利方面的进展,应用和局限性.
主要方法:
- 关于动物面部表情分析和疼痛尺度的现有文献的综述.
- 探索人工智能 (AI) 应用,包括计算机视觉和机器学习.
- 分析数据,包括面部表情,肢体语言,发音和生理信号.
主要成果:
- 基于人工智能的APR提供了精确和客观的动物疼痛评估.
- APR模型可以分析多式联络数据,以进行全面的疼痛评估.
- 在开发用于非语言疼痛检测的AI工具方面取得了重大进展.
结论:
- 自动疼痛识别 (APR) 是兽医和动物福利领域的重大进展.
- 克服数据限制和确保道德实践对于APR开发至关重要.
- APR有望改善疼痛管理和整体动物福祉.
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