HabiTrak: low-cost, wireless home cage health and activity monitoring.

项目摘要 目前可用的用于自动化家庭笼子健康和行为的商业系统 对小动物研究模型的监测通常是昂贵的，而且不是普遍有效的 与研究人员现有的笼子系统进行比较。在这个SBIR第二阶段项目中，Vulintus， Inc.将继续开发和验证“HabiTrak”，这是一种微型低成本无线设备，用于 在任何标准商业住宅内持续监测小动物的健康和活动 凯奇。HabiTrak的设计利用了传感器小型化的最新进展，将 一套热像仪和环境传感器，采用坚固耐用的小型包装，旨在 无线放置在家中的笼子里。Vulintus的第二阶段活动包括完成 用于实时访问的交互式在线生态系统的硬件设计和开发 HabiTrak数据。同时，HabiTrak将在三种不同的生物医学领域进行Beta测试 在德雷塞尔大学、科罗拉多博尔德大学和科罗拉多州大学的研究 德州对阵达拉斯。Beta测试将有助于确定HabiTrak的准确性和实用性 测量，并指导开发协议，使HabiTrak可以最容易 部署到现有的研究机构。在第二阶段结束时，一个完全发展的 HabiTrak系统将为研究人员和间隔室提供一个前所未有的窗口 他们的动物的健康和活动，特别是需要严格监督的模型，并将 帮助识别和控制可能掩盖重大发现的滋扰因素。

Project Abstract Currently available commercial systems for automated home cage health and behavioral monitoring of small animal research models are typically expensive and do not work universally with researchers’ existing vivarium cage systems. Under this Phase II SBIR project, Vulintus, Inc. will continue to develop and validate “HabiTrak”, a miniature low-cost, wireless device for continuously monitoring small animal health and activity inside any standard commercial home cage. The HabiTrak design leverages recent advances in sensor miniaturization to combine a suite of thermographic and environmental sensors in a small, robust package designed to be placed wirelessly inside the home cage. Vulintus’ Phase II activities include finalization of the hardware design and development of an interactive online ecosystem for real-time access to HabiTrak data. Concurrently, HabiTrak will undergo beta testing in three diverse biomedical research studies at Drexel University, the University of Colorado Boulder, and the University of Texas at Dallas. Beta testing will help to establish the accuracy and utility of HabiTrak measurements, and guide the development of protocols for how HabiTrak can be most easily deployed into existing research institutions. At the conclusion of this Phase II, a fully developed HabiTrak system will provide researchers and vivariums with an unprecedented window into their animals’ health and activity, particularly for models requiring intensive supervision, and will help to identify and control for nuisance factors that may obscure significant discoveries.