探索马的行为:可穿戴传感器数据和可解释的人工智能用于增强分类
Bekir Cetintav1, Ahmet Yalcin2
1Veterinary Faculty, Department of Biostatistics, Burdur Mehmet Akif Ersoy University, Istiklal Campus, 15030 Burdur, Türkiye.
Journal of equine veterinary science
|April 12, 2025
概括
使用SHAP的可解释AI (XAI) 增强了使用可穿戴传感器的马类行为分类. 这项技术准确地识别了马的行为,改善了福利和健康监测.
科学领域:
- 动物行为 动物行为
- 机器学习 机器学习
- 可穿戴技术是可穿戴的技术.
背景情况:
- 先进的监测是马群福利和健康的关键.
- 可穿戴式传感器捕获了详细的马匹运动数据.
- 需要解释性AI (XAI) 来解释复杂的行为模型.
研究的目的:
- 将可穿戴传感器数据与XAI集成,用于马类行为分类.
- 在马研究中提高AI模型的可解释性.
- 识别关键的传感器特征,以区分马的行为.
主要方法:
- 利用了来自18匹马的开源数据集来研究马匹的行为.
- 使用机器学习模型 (随机森林,KNN,XGBoost) 来进行多类分类.
- 应用SHAP (沙普利添加式解释) 进行特征归属分析.
主要成果:
- 随机森林在分类17种马类行为方面实现了82.3%的准确性.
- SHAP分析确定了传感器的贡献:加速计用于机动,磁力计用于定向,陀螺仪用于动态运动.
- 特定的传感器特征与跳跃,站立和摇头等行为有关.
结论:
- XAI,特别是SHAP,显著提高了人类行为AI模型的可解释性.
- 这种方法为实时监测,压力检测和兽医干预提供了可操作的见解.
- 这项研究为马类行为分析中可解释的AI建立了新的基准,增强了信任和适用性.
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