Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
The rising number of senior citizens is approaching an all time high. With this comes a rising number of common chronic conditions where treatment is costly and time consuming. Frequently these conditions make it unsafe for older adults to live independently. This creates a burden on loved ones because situations arise which require assistance. Slip & falls, wandering, and daily health monitoring are the top burdens loved ones face with an aging adult. Today, however, these burdens can be minimized by using smartphone technology. Modern devices are capable of automatically recognizing, reporting, and remembering these situations. The proposed system is a collection of several applications that enable this functionality for Android powered devices. These applications focus on monitoring falls, wandering, and storing health information in the users daily life. The software achieves these tasks by gathering and analyzing data from various sensors both on and off the device. Several algorithms are applied to monitor and report dangerous events. The algorithms range from learning networks to timing based thresholds. If a dangerous event is detected it can be easily canceled by the user in order to reduce false positives. After confirmation, or lack of, the user's loved ones are promptly notified to further assess the situation. By using Social Monitoring, false positive to costly emergency medical professionals are kept to the bare minimum. Thus eliminating the need for paid monitoring services. The system also provides an API to allow for other developers to integrate with the event analysis. Once in place, a wealth of information can be recorded and used to help identify further problems and solutions.
android, ehealth, fall detection, medical, mobile, monitoring
Date of Defense
March 26, 2012.
A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science.
Includes bibliographical references.
Gary Tyson, Professor Directing Thesis; Zhenghao Zhang, Committee Member; Zhenhai Duan, Committee Member.
Florida State University
Use and Reproduction
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them.