The use of behavioural biometrics, 3D kinematics and infrared thermography to assess affective and biological states in livestock

使用行为生物识别、3D 运动学和红外热成像技术评估牲畜的情感和生物状态

• 批准号: RGPIN-2021-02890
• 负责人: Bench, Clover
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743330
• 关键词: use; behavioural; biometrics; 3D; kinematics

As a result of the move away from the Five Freedoms paradigm of welfare assessment, towards a Five Domains model, the field of ethology is challenged to develop valid, reliable, and feasible ways to evaluate a variety of affects experienced by animals. Livestock species are at the forefront of this development as an integral part of the global food system. Since 2016, studies have identified changes in facial action units through the use of grimace scales in animals to measure negative affects, however there are limitations to the use of grimace scales in livestock. For example, individual expression and magnitude of pain can vary between animals, which grimace scales have difficulty differentiating. For these and other reasons, grimace scales for pain assessment in some livestock species, such as pigs, have not been fully validated. My research program is unique as the only program investigating micro-behaviour biometrics in livestock as part of automated technology platforms for use in precision agriculture. The overall aim of my research program has been to better understand how qualitative and quantitative differences in livestock micro-behaviour biometrics can be incorporated into automated systems and combined with machine learning for a variety of research and on-farm applications. In addition to the use of automated infrared thermography platforms to detect micro-behaviour biometrics alongside thermal profiles as part of early disease detection, my research has also used 3D kinematic motion capture technology to better understand the biomechanics of micro-behaviours. For example, the kinematics of micro-behaviours exhibited during estrus, which confirmed micro-movements are better at detecting changes in biological functioning. However, an area that has not been explored is the use of micro-behaviour biometrics to assess affective states and facial expressions in livestock. The focus of the proposed research is to answer an emerging question in animal welfare: Are micro-facial expressions and other micro-behaviour biometrics an epiphenomenon for pain or other affective states in livestock? If so, what are the underlying physiological mechanisms? The short-term objectives of this research are: 1) to characterize the micro-facial expressions associated with positive and negative affective states, and 2) to ascertain the physiological mechanisms (pathway) underlying micro-facial expressions as affective biometrics using 3D kinematic analysis, thermal imaging, and genomic analysis. The proposed research will clarify the role physiology plays in driving micro-facial expressions in response to different types of stimuli experienced by pigs and not only strives to find a solution to current problems with the use of pain grimace scales, but also suggests it can be done using automation integrating machine learning as the Canadian agriculture sector moves towards greater inclusion of precision systems.

从福利评估的五个自由范式转向五个领域模型之后，伦理学领域面临开发有效，可靠和可行的方法来评估动物所经历的各种影响。作为全球粮食系统不可或缺的一部分，牲畜物种处于这一发展的最前沿。自2016年以来，研究已经通过在动物中使用鬼脸量表来衡量负面影响，从而确定了面部动作单位的变化，但是在牲畜中使用鬼脸尺度存在局限性。例如，动物之间的个体表达和疼痛的大小可能会有所不同，鬼脸量表很难区分。由于这些和其他原因，在某些牲畜物种（例如猪）中进行疼痛评估的鬼脸量表尚未得到充分验证。我的研究计划是唯一的计划，该计划是牲畜中的微型行为生物识别技术，这是用于精确农业的自动化技术平台的一部分。我的研究计划的总体目的是更好地了解如何将牲畜微型生物识别的定性和定量差异纳入自动化系统中，并与机器学习一起以进行各种研究和农场应用。除了使用自动红外热成像平台在早期疾病检测的一部分中检测微作用生物识别技术，我的研究还使用了3D运动运动捕获技术来更好地了解微型行为的生物力学。例如，在发情期间表现出的微观行为的运动学，证实微动物可以更好地检测生物功能的变化。但是，尚未探索的区域是使用微行为生物识别技术来评估牲畜中情感状态和面部表情。拟议的研究的重点是回答动物福利中的一个新兴问题：微观表情和其他微型行为生物识别学是痛苦或牲畜中其他情感状态的epiphenomenon吗？如果是这样，基本的生理机制是什么？这项研究的短期目标是：1）表征与正面和负面情感状态相关的微种族表达，以及2）使用3D运动分析，热成像和基因组分析来确定微种族表达的生理机制（途径）为情感生物特征。拟议的研究将阐明生理学在响应猪经历的不同类型的刺激中驱动微种族表达方面的作用，不仅努力为使用疼痛鬼脸量表找到目前问题的解决方案，而且还暗示可以使用自动化进行自动化的机器学习，因为加拿大农业部门朝着更大的纳入精确的系统中移动。