基于振动的非接触式活动分类,用于使用调节束IMU传感器件监控家庭子
Pieter Try1, René H Tolba2, Marion Gebhard1
1Department of Electrical Engineering and Applied Sciences, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
这项研究引入了一种非接触式,基于振动的方法,用于归类家庭中的老鼠身体活动. 该系统使用一种新的传感器和机器学习来准确监测行走和休息等行为.
科学领域:
- 动物行为 动物行为
- 生物传感技术的技术
- 机器学习在生物学中的应用
背景情况:
- 准确监测实验室动物的行为对于研究至关重要.
- 对于家庭子环境,需要使用非侵入性,持续的监测方法.
- 现有的方法可能是侵入性的或范围有限的.
研究的目的:
- 开发和验证一种基于振动的非接触方法来分类小鼠的身体活动.
- 为了增强信号检测,使用一种新的调光束传感器.
- 实施一个机器学习模型,以准确地分类活动.
主要方法:
- 开发了一种与六轴惯性测量单元 (IMU) 集成的调节光束传感装置,以捕获子振动.
- 多级别离散波形变换 (MLDWT) 用于从振动信号中提取特征.
- 一个卷积神经网络长期短期记忆 (CNN-LSTM) 模型根据提取的特征对活动进行了分类.
- 使用基于摄像头的系统 (EthoVision XT) 获取地面真相数据.
主要成果:
- 该系统在9秒的时间范围内实现了86.81%的准确性和0.798的平均F1得分,用于分类活动,包括休息,静止活动,步行,食器中的活动和饮酒.
- 调节光束传感器提高了信号噪声比 (SNR) 的20到40倍.
- 该方法证明了长期行为模式的可靠复制,如睡眠和适应,与参考方法相比.
结论:
- 拟议的基于振动的监控系统提供了一个低成本的非接触式解决方案,用于在家用子中准确地分类小鼠活动.
- 这项技术可以在畜牧环境中有效的长期行为监测.
- 该方法有可能通过提供对动物福利和行为的详细见解来推进研究.
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