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OWLS : Observational Wireless Life-enhancing System

In personalized care, there is an increasing necessity to allow people with physical limitations or disabilities to continue independent quotidian activities and functions. Socially assistive robotics technologies for individuals, who have been affected by age-related disabilities and similar types of disorders, have become popular options for facilitating natural independence and uninterrupted mobility. Among its diverse applications, therapeutic robotic systems have been utilized in the muscular physiotherapies for movement training, wrist and arm treatment for injuries and overexertions, and other therapies. Applications of wearable sensors for human assistance or health monitoring have been also gaining popularity in recent years. Wireless wearable sensor systems enable proactive personal health management and the ubiquitous monitoring of vital signs to keep an active watch on immediate health conditions. In this paper, we develop a system, called OWLS, where multiple wearable sensors, software agents, robots and health analysis technology, have been integrated into a single personal therapy solution (SPTS). In this system, each robot has the “smart” intelligence to analyze and process its own unique data, as well as the collected sensor data, and is able to aggregate the information received from the other system robots. Our system uses a reinforcement learning algorithm to make decisions about the user’s current health conditions, and to take appropriate actions, as necessary (i.e, contacting outside parties). Our experimental results have shown that the reinforcement learning algorithm developed with our system allows the robots to accurately detect immediate signs for the correct determination of the health conditions in monitored users. We posit that the approach of non-invasive monitoring, when combined with an alert system, will make this a desirable SPTS in future health care.