Detecting Medical Emergencies in Isolated Older Adults Living Alone in Rural Areas

摘要 独自生活在农村房屋中的孤寡老人面临着在没有帮助的情况下陷入医疗困境的风险。A农村 豪斯可以与邻居隔离，这些邻居可以很容易地检查他们的福祉。当这些人变成 老年人，他们可以有很高的偏好留在家里，只要他们相信自己可以照顾 他们自己。在与世隔绝的农村，独自一人和处于困境中的老年人可能处于生死存亡的境地 回家。在一所与世隔绝的农村房子里，很多天后才会有人决定开车去他们所在的地方 去看看那位老人。该项目为农村孤寡老人开发了一种低成本的监测解决方案 安全地过着独立的生活。第一阶段SBIR项目将开发一种无线室内跟踪系统，以 通过典型农舍的几面墙来探测一个人的位置。将不需要布线，也不需要电缆 在房子里安装一个复杂的传感器阵列。安装成本不贵。系统将 有备用电池和协议，可在长时间停电时运行。它还将有一个手机备份 如果电话线路出现故障，您可以选择通信选项。该系统将识别老年人24的动作 一天几个小时呆在家里。机器学习(ML)算法可以从日常工作中检测到异常。 该产品旨在成为当今使用的家庭警报系统的可选附件，并可与 多家家庭警报系统供应商。它将与可穿戴按钮并行操作，发出警报信号。 一旦系统识别到发生了遇险事件，它将激活警报系统，方式与 可穿戴式按钮按键。将遵循相同的警报协议。在这些系统中，操作员首先会尝试 与拥有供应商家中警报系统扬声器的人交谈。如果他们不能交流 在这个人的帮助下，他们开始通过当地人的电话名单来检查房子。第一阶段将 开发和测试无线室内跟踪系统和机器学习(ML)算法。

Abstract Isolated older persons living alone in a rural house are at risk of being in medical distress without help. A rural house can be isolated from neighbors who can easily check on their well-being. As these people become elderly, they can have a high preference to stay in their home as long they believe they can care for themselves. An elderly person alone and in distress can be in a life and death situation in an isolated rural home. In an isolated rural house, many days can pass before someone decides to drive to their location to check on the elderly person. This project develops a low-cost monitoring solution for isolated rural elderly to safely lead independent lives. The phase I SBIR project will develop a wireless indoor tracking system to detect a person's location through several walls of a typical rural house. There will be no cables to run or need to install a complex array of sensors through the house. The cost of installation inexpensive. The system will have battery backup and protocols to operate over long power outages. It will also have a cell phone backup communication option should phone lines be down. The system will identify motions of the elderly person 24 hours a day in the house. Machine Learning (ML) Algorithms can detect abnormalities from their daily routine. The product is intended to be an optional accessory to in-home alert systems in use today and work with multiple vendors of in-home alert systems. It will operate in parallel with wearable buttons to signal an alert. Once the system identifies a distress event has occurred it will activate the alert system the same way as a wearable button press. The same alert protocol would be followed. In these systems an operator would first try to talk to the person with a speaker phone of the vendors' in-home alert system. If they cannot communicate with the person, they start working through a call list of local people to check on the home. The phase I will develop and test the wireless indoor tracking system and the Machine Learning (ML) Algorithms.