ITR: An Infrastructure for Veterinary Telemedicine - Proactive Herd Health Management for Disease Prevention from Farm to Market

ITR：兽医远程医疗基础设施 - 主动牛群健康管理，预防从农场到市场的疾病

• 批准号: 0325921
• 负责人: Daniel Andresen
• 金额: --
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-10-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325921&HistoricalAwards=false
• 关键词: ITR; Infrastructure; Veterinary; Telemedicine; Proactive

The goal of this project is to research and develop a prototype,technology-rich infrastructure to support economical continuous monitoringof cattle state of health via remote and wearable devices that interactwith a veterinary health information network. Wireless stations,distributed databases, and physiological assessment algorithms will play akey role in this environment. These systems are improving the ability ofthe livestock industry to react to and predict disease onset and itsepidemiological spread, whether from natural or terrorist events. Trendanalysis and health prediction lessons learned from this effort haveapplication to human state-of-health prediction and disease spread inhuman populations.The researchers are placing Bluetooth-compliant monitoring stations nearcattle congregation points, such as feed bunks and watering troughs. Thesestations upload data from environmental sensors, Bluetooth-enabled deviceswith global positioning capability worn by the animals, andwearable/remote biomedical sensors. Local algorithms perform rapid dataanalysis prior to uploading ranch summary data to regional databases,where the data is correlated with information provided by nearbyproducers. Significant findings are then immediately broadcast to regionalveterinarians, producers, and authorities. In order to accomplish this, the research addresses a number of important information technology issues, including scheduling algorithms that adaptively determine where data analysis should occur and which areas require more in-depth analysis; prioritization algorithms for multiple wireless data streams with near-real-time constraints; and area-appropriate data mining techniques to find problem indicators.The interdisciplinary project is addressing a critical need within the agriculturaland national communities: applied research that allows the animalsciences industry to react to and predict disease onset in cattle and itsepidemiological spread. Through the development of a distributed softwareinfrastructure, a comprehensive physiological monitoring toolset, and newprocessing algorithms (e.g., for data storage, fusion, andinterpretation), we are helping to improve the financial stability of thelivestock industry while simultaneously raising our level of preparednessfor epidemiological disasters, whether from natural or terrorist events.

该项目的目标是研究和开发一个原型，技术丰富的基础设施，以支持经济的连续监测牛的健康状况，通过远程和可穿戴设备与兽医健康信息网络进行交互。 无线站、分布式数据库和生理评估算法将在这一环境中发挥关键作用。这些系统正在提高畜牧业应对和预测疾病发作及其流行病学传播的能力，无论是自然事件还是恐怖事件。从这项工作中获得的趋势分析和健康预测经验可以应用于人类健康状况预测和疾病传播。研究人员正在牛聚集点附近，如饲料槽和饮水槽，放置符合蓝牙标准的监测站。这些站点上传来自环境传感器、动物佩戴的具有全球定位功能的蓝牙设备以及可穿戴/远程生物医学传感器的数据。本地算法在将牧场汇总数据上传到区域数据库之前进行快速数据分析，在区域数据库中，数据与附近生产者提供的信息相关联。重要的发现会立即向地区兽医、生产者和当局广播。 为了实现这一点，研究解决了一些重要的信息技术问题，包括调度算法，自适应地确定数据分析应该发生在哪里，哪些领域需要更深入的分析，优先级算法的多个无线数据流与近实时的约束;和适合地区的数据挖掘技术，以找到问题指标。跨学科项目正在解决农业和民族社区的一个关键需求：应用研究，使动物科学行业能够对牛的疾病发作及其流行病学传播做出反应和预测。 通过开发分布式软件基础设施、全面的生理监测工具集和新的处理算法（例如，我们正在帮助改善畜牧业的财务稳定性，同时提高我们对流行病灾害的防范水平，无论是自然灾害还是恐怖主义事件。