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

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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.