Department of Computer Science and Information Engineering, Healthy Aging Research Center, Chang Gung University, Taiwan, ROC.
Med Eng Phys. 2013 Feb;35(2):263-8. doi: 10.1016/j.medengphy.2011.09.010. Epub 2011 Oct 5.
The purpose of this study is to integrate wireless sensor technologies and artificial neural networks to develop a system to manage personal frailty information automatically. The system consists of five parts: (1) an eScale to measure the subject's reaction time; (2) an eChair to detect slowness in movement, weakness and weight loss; (3) an ePad to measure the subject's balancing ability; (4) an eReach to measure body extension; and (5) a Home-based Information Gateway, which collects all the data and predicts the subject's frailty. Using a furniture-based measuring device to provide home-based measurement means that health checks are not confined to health institutions. We designed two experiments to obtain optimum frailty prediction model and test overall system performance: (1) We developed a three-step process to adjust different parameters to obtain an optimized neural identification network whose parameters include initialization, L.R. dec and L.R. inc. The post-process identification rate increased from 77.85% to 83.22%. (2) We used 149 cases to evaluate the sensitivity and specificity of our frailty prediction algorithm. The sensitivity and specificity of this system are 79.71% and 86.25% respectively. These results show that our system is a high specificity prediction tool that can be used to assess frailty.
本研究旨在整合无线传感器技术和人工神经网络，开发一个自动管理个人虚弱信息的系统。该系统由五个部分组成：（1）电子秤，用于测量对象的反应时间；（2）电子椅，用于检测运动缓慢、虚弱和体重减轻；（3）电子垫，用于测量对象的平衡能力；（4）电子伸手，用于测量身体伸展；（5）基于家庭的信息网关，用于收集所有数据并预测对象的虚弱状况。使用基于家具的测量设备提供家庭测量意味着健康检查不仅限于医疗机构。我们设计了两个实验来获得最佳的虚弱预测模型并测试整体系统性能：（1）我们开发了一个三步过程来调整不同的参数以获得优化的神经识别网络，其参数包括初始化、L.R. dec 和 L.R. inc。后处理识别率从 77.85%提高到 83.22%。（2）我们使用 149 个病例评估我们的虚弱预测算法的敏感性和特异性。该系统的敏感性和特异性分别为 79.71%和 86.25%。这些结果表明，我们的系统是一种高特异性的预测工具，可用于评估虚弱状况。
