Stefan Almer

Mobile Applications for Fall Detection: in the Area of Ambient Assisted Living Stefan Almer

With an increasing population of elderly people the number of falls and fall-related injuries is on the rise. This will cause changes for future health care systems, and both fall detection and fall prevention will pose a major challenge. Ambient Assisted Living (AAL) is a research area in which concepts and information systems for assisting elderly individuals are developed. Fall detection, as an important discipline of AAL, investigates a broad range of approaches including wearable devices. With their growing popularity, mobile devices with their embedded motion sensors, their software capabilities and cost-efficiency are well-suited for fall detection. A test framework for collecting and analyzing data regarding fall detection is presented. The framework consists of a RESTful Web service, a relational database and a Web-based back end. It offers an open interface to support a variety of devices. The system architecture is based on the state-of-the-art theoretical background of AAL and on the evaluation of an existing software. In order to test the framework, a mobile device client recording accelerometer and gyroscope sensor data is implemented on the iOS platform.