Yu Zhenhua, Han Yalou, Cha Lukas, Chen Shihong, Wang Zeyu, Zhang Yang
Department of Mechanical Engineering, Imperial College London, London, United Kingdom.
Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Front Robot AI. 2024 Nov 21;11:1441960. doi: 10.3389/frobt.2024.1441960. eCollection 2024.
In the realm of precision cattle health monitoring, this paper introduces the development and evaluation of a novel wearable continuous health monitoring device designed for cattle. The device integrates a sustainable solar-powered module, real-time signal acquisition and processing, and a storage module within an animal ergonomically designed curved casing for non-invasive cattle health monitoring. The curvature of the casing is tailored to better fit the contours of the cattle's neck, significantly enhancing signal accuracy, particularly in temperature signal acquisition. The core module is equipped with precision temperature sensors and inertial measurement units, utilizing the Arduino MKR ZERO board for data acquisition and processing. Field tests conducted on a cohort of ten cattle not only validated the accuracy of temperature sensing but also demonstrated the potential of machine learning, particularly the Support Vector Machine algorithm, for precise behavior classification and step counting, with an average accuracy of 97.27%. This study innovatively combines real-time temperature recognition, behavior classification, and step counting organically within a self-powered device. The results underscore the feasibility of this technology in enhancing cattle welfare and farm management efficiency, providing clear direction for future research to further enhance these devices for large-scale applications.
在精准牛健康监测领域，本文介绍了一种专为牛设计的新型可穿戴连续健康监测设备的开发与评估。该设备将可持续太阳能供电模块、实时信号采集与处理以及存储模块集成在一个符合动物人体工程学设计的弯曲外壳中，用于牛的非侵入式健康监测。外壳的曲率经过定制，以更好地贴合牛颈部的轮廓，显著提高信号准确性，尤其是在温度信号采集方面。核心模块配备了精密温度传感器和惯性测量单元，利用Arduino MKR ZERO板进行数据采集和处理。对十头牛进行的现场测试不仅验证了温度传感的准确性，还展示了机器学习，特别是支持向量机算法在精确行为分类和步数计数方面的潜力，平均准确率为97.27%。本研究创新性地在一个自供电设备中有机结合了实时温度识别、行为分类和步数计数。结果强调了该技术在提高牛福利和农场管理效率方面的可行性，为未来进一步改进这些设备以实现大规模应用的研究提供了明确方向。
