Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey.
IT Department, Garanti Technology, 34212 Istanbul, Turkey.
Sensors (Basel). 2017 Jun 23;17(7):1487. doi: 10.3390/s17071487.
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.
自动检测跌倒事件对于为事故受害者提供快速医疗援助至关重要，尤其是当受伤导致失去意识时。优化移动应用程序的能耗对于延长智能手机的使用时间非常重要，特别是那些 24/7 都在后台运行的应用程序。为了在不影响跌倒检测性能的情况下提高能效，我们提出了一种新的三层次架构，将简单的阈值方法与机器学习算法相结合。所提出的方法在一个名为 uSurvive 的移动应用程序上实现，适用于 Android 智能手机。它作为后台服务运行，监控日常生活中一个人的活动，并在检测到跌倒时自动向适当的当局和/或用户定义的联系人发送通知。所提出的方法的性能是根据跌倒检测性能和能耗来评估的。在两款不同型号的智能手机上进行的实际性能测试表明，与仅使用机器学习的解决方案相比，我们的具有特征减少功能的三层架构可以节省高达 62%的能源。除了节省能源之外，混合方法的准确率达到了 93%，优于阈值方法，也优于仅使用机器学习的解决方案。
