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基于机器学习的传感器数据融合用于动物监测:范围审查

Carlos Alberto Aguilar-Lazcano1, Ismael Edrein Espinosa-Curiel1, Jorge Alberto Ríos-Martínez2

  • 1CICESE-UT3, Tepic 63173, Mexico.

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概括

传感器融合和人工智能正在通过分析传感器数据以获取行为和健康见解来推进动物研究. 这项技术为改善动物福利,生产和保护工作提供了机会.

关键词:
动物电脑互动动物电脑互动动物动物动物动物动物动物机器学习是机器学习.传感器 传感器 传感器 传感器融合传感器 融合传感器 融合传感器

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科学领域:

  • 动物行为和动物福利
  • 生物技术和传感器技术
  • 在动物科学中的机器学习

背景情况:

  • 物联网和人工智能 (AI) 等技术进步正在改变科学研究,包括动物研究.
  • 传感器件和人工智能驱动系统能够进行复杂的数据收集和分析,以识别动物行为,情绪状态和个人识别.
  • 本综述侧重于 2011 年至 2022 年期间发表的动物研究中应用的传感器融合技术.

研究的目的:

  • 审查和分析动物研究中传感器融合算法的当前状态.
  • 确定传感器融合应用的趋势,目标物种和数据分析水平.
  • 突出未来在动物福利和相关领域的传感器融合研究的机会.

主要方法:

  • 2011年至2022年出版的英语文章的系统文献搜索.
  • 纳入标准用于过263个获取的文章,将其减少到23个符合分析条件的文章.
  • 传感器融合算法的分类:原始/低级,特征/中级和决策/高级.

主要成果:

  • 传感器融合算法分布在原始 (26%),特征 (39%) 和决策 (34%) 级别.
  • 姿势和活动检测是最常见的研究重点.
  • 牛 (32%) 和马 (12%) 是主要研究的物种;在所有融合水平上都使用了加速度计.

结论:

  • 传感器融合在动物研究中的应用还处于新生阶段,尚未得到充分的探索.
  • 通过传感器融合整合运动和生物识别数据,为动物福利应用提供了巨大的潜力.
  • 传感器融合与机器学习相结合,可以增强对动物行为的理解,从而提高福利,生产效率和保护.