机器学习方法和视觉观察,用加速计信号对放牧牛的行为进行分类
Ira Lloyd Parsons1,2, Brandi B Karisch3, Amanda E Stone3
1Quantitative Ecology and Spatial Technologies Laboratory, Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Starkville, MS 39762, USA.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
动物加速度计创造独特的行为特征,通过机器学习准确地分类. 这项研究优化了信号处理,以精确识别动物行为,并通过更大的光滑窗口和牧场内观测提高了准确性.
科学领域:
- 动物行为分析 动物行为分析
- 机器学习应用程序 机器学习应用程序
- 可穿戴式传感器技术的技术.
背景情况:
- 动物上的加速度计产生了不同的行为数据.
- 机器学习,就像随机森林决策树,可以对这些信号进行分类.
- 了解加速度计信号分离是准确的行为分类的关键.
研究的目的:
- 为了在动物基本行为之间区分加速度计信号.
- 为了优化信号预处理窗口大小用于行为分类.
- 确定准确的机器学习模型所需的最小观测值.
主要方法:
- 在10只杂交牛 (Bos taurus indicus) 上使用三轴加速度计 (40 Hz) 和GPS项圈.
- 分析了与离散行为相对应的加速度计信号的功能差异,特别是放牧.
- 评估了光滑窗口大小和观察数量对分类准确性的影响.
主要成果:
- 从加速度计信号中确定了明显的行为特征,特别是在放牧时 (俯卧姿势),从加速度计信号中确定.
- 一个10秒的光滑窗口大小显著提高了分类准确性 (p < 0.05).
- 将观测量减少到50%以下的准确性降低了,而在牧场观测提高了准确性和精确性.
结论:
- 加速度计数据,当优化处理时,可以准确地分类不同的动物行为.
- 信号预处理参数,如窗口大小和观察量,对于模型性能至关重要.
- 在牧场的观测提供了与领戴式视频相比,对行为分类的更高的准确性和精确性.
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