Helal Abdelsalam, Cook Diane J, Schmalz Mark
Computer and Information Science and Engineering Department, University of Florida, Gainesville, Florida, USA.
J Diabetes Sci Technol. 2009 Jan;3(1):141-8. doi: 10.1177/193229680900300115.
Researchers and medical practitioners have long sought the ability to continuously and automatically monitor patients beyond the confines of a doctor's office. We describe a smart home monitoring and analysis platform that facilitates the automatic gathering of rich databases of behavioral information in a manner that is transparent to the patient. Collected information will be automatically or manually analyzed and reported to the caregivers and may be interpreted for behavioral modification in the patient.
Our health platform consists of five technology layers. The architecture is designed to be flexible, extensible, and transparent, to support plug-and-play operation of new devices and components, and to provide remote monitoring and programming opportunities.
The smart home-based health platform technologies have been tested in two physical smart environments. Data that are collected in these implemented physical layers are processed and analyzed by our activity recognition and chewing classification algorithms. All of these components have yielded accurate analyses for subjects in the smart environment test beds.
This work represents an important first step in the field of smart environment-based health monitoring and assistance. The architecture can be used to monitor the activity, diet, and exercise compliance of diabetes patients and evaluate the effects of alternative medicine and behavior regimens. We believe these technologies are essential for providing accessible, low-cost health assistance in an individual's own home and for providing the best possible quality of life for individuals with diabetes.
长期以来，研究人员和医学从业者一直寻求能够在医生办公室之外持续自动监测患者的能力。我们描述了一个智能家居监测与分析平台，该平台能够以患者不知情的方式自动收集丰富的行为信息数据库。收集到的信息将自动或手动进行分析，并报告给护理人员，还可用于解读患者行为以进行行为矫正。
我们的健康平台由五个技术层组成。其架构设计灵活、可扩展且透明，以支持新设备和组件的即插即用操作，并提供远程监测和编程机会。
基于智能家居的健康平台技术已在两个物理智能环境中进行了测试。在这些已实现的物理层中收集的数据由我们的活动识别和咀嚼分类算法进行处理和分析。所有这些组件在智能环境测试床中对受试者都得出了准确的分析结果。
这项工作是基于智能环境的健康监测与辅助领域重要的第一步。该架构可用于监测糖尿病患者的活动、饮食和运动依从性，并评估替代医学和行为方案的效果。我们相信这些技术对于在个人家中提供可及的低成本健康辅助以及为糖尿病患者提供尽可能好的生活质量至关重要。
