ITR: Veterinary Telemedicine - Proactive Herd Health Management for Disease Prevention from Farm to Market
This project is funded by the NSF under the ITR program, award number CCR/ITR-0205487.Goal The goal of this project is to research and develop the technological and sociological infrastructure to support intelligent, mobile medical monitoring devices that continuously assess cattle state of health in concentrated and distributed herds. These monitoring systems will improve the ability of the animal sciences industry to react to and predict disease onset and its epidemiological spread, whether from natural or terrorist events. Trend analysis, information storage, and health prediction lessons learned from this effort will have immediate application to distributed medical systems targeted at assessing and predicting human state of health and the spread of disease in human populations.
Our plan is to place Bluetooth-compliant monitoring stations near cattle congregation points, such as feed bunks and watering troughs. These stations will upload data from nearby environmental sensors, Bluetooth-enabled devices with global positioning capability that are worn by the animals, and wearable/remote biomedical sensors. Initial algorithms will perform rapid analysis on local data prior to uploading summary data for the ranch to regional databases so that these data can be correlated with data provided by other producers. Significant findings can then be immediately broadcast to appropriate medical personnel and producers.
Use scenarios. Assume the system has evolved to the point where durable, small sensors (e.g., to report animal identity, position, temperature, blood pressure, and other physiological data) are inexpensive enough to warrant wide use. Data are logged on individual sensors and periodically uploaded to Bluetooth-enabled feed troughs. A precursory analysis determines animal state of health and notes potential trouble spots, and the data are then subjected to extensive offline analysis to identify more subtle trends. These results are integrated with previous sensor data, weather reports, weather forecasts, infrared camera images, and prior health assessments. The producer can then go out to the feedlot with a list of potential trouble situations that are dynamically updated on his WAP-enabled cell phone. In the meantime, authenticated disease reports are aggregated into a daily summary prepared for regional veterinarians.
Research issues. This project must address a large number of research issues:
Impact. This capability has tremendous potential for veterinary medicine, the agriculture industry, and in the long term, human medicine and quality of life. As recently demonstrated by the devastating impact of "hoof and mouth" disease and "Mad Cow" disease on the European farming industry, disease epidemiology needs much greater support at the level of the individual farm. Economic benefits to producers will also be significant, since these systems will enable them to assess and treat animals sooner, preventing further damage to individual animals as well as the spread of disease.
- Biomedical sensors, GPS receivers, and Bluetooth-enabled devices worn by the cattle must be developed, linked, and packaged robustly for survival in difficult environments.
- Receiver systems must be developed to manage wireless traffic and prioritize overlapping signal streams.
- Scheduling algorithms must be developed to adaptively determine where data analysis should occur and which areas require more in-depth analysis.
- Algorithms must be created that search for data patterns that indicate if trouble is looming or has already arrived.
- The system must be designed in such a way that it is economically feasible for typical farmers and producers, many of which maintain a tenuous hold on financial stability.
- Security mechanisms must be included that maintain the confidentiality of economic data and herd health information associated with individual farms. The privacy of the individual farmer must be maintained without compromising the data's statistical validity.
For further information, contact Dr. Daniel Andresen at 785-532-6350 or dan _at_ ksu.edu.