Developing a Fall Detection Technology for Mobility and System Level

Smartphone’s are programmable and embed various sensors; these phones have the potential to change the way how healthcare is delivered. Fall detection is definitely one of the possibilities. Injuries due to falls are dangerous, especially for elderly people, diminishing the quality of life or even resulting in death. This study presents the implementation of a fall detection prototype for the Android-based platform. The proposed system has three components: sensing the accelerometer data from the mobile embedded sensors, learning the relationship between the fall behavior and the collected data, and alerting preconfigured contacts through message while detecting fall. We adopt different fall detection algorithms and conduct various experiments to evaluate performance. The results show that the proposed system can recognize the fall from human activities, such as sitting, walking and standing, with 72.22% sensitivity and 73.78% specificity. The experiment also investigates the impact of different locations where the phone attached. In addition, this study further analyzes the trade-off between sensitivity and specificity and discusses the additional powers consumption of the devices.