School of Computer Science & Engineering, South China University of Technology, Guangzhou 510006, China.
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510006, China.
Sensors (Basel). 2017 Sep 13;17(9):2096. doi: 10.3390/s17092096.
Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user's body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system.
跌倒对老年人来说是一种非常危险的情况，因为它可能导致骨折、脑震荡和其他伤害。如果没有及时的救援，跌倒甚至可能危及生命。现有的基于光学传感器的跌倒监测系统存在一些缺点，例如监测范围有限，用户携带不便。此外，仅基于加速度计的跌倒检测系统常常错误地将一些日常生活活动（ADL）判断为跌倒，导致跌倒检测的准确性较低。我们提出了一个由高度便携的传感器单元组成的人体跌倒监测系统，该传感器单元包括三轴加速度计、三轴陀螺仪和三轴磁力计以及一部手机。通过这些传感器的数据，我们获得了加速度和欧拉角（偏航、俯仰和滚动），它们代表了用户身体的方向。然后，我们使用提出的跌倒检测算法根据加速度和欧拉角来检测跌倒。通过这个监测系统，我们设计了一系列模拟跌倒和 ADL，并通过将传感器放置在受试者的肩部、腰部和脚部来进行实验。通过实验，我们重新确定了准确检测跌倒所需的加速度阈值，并通过比较不同身体部位的检测性能来验证放置传感器的最佳身体位置。我们还将这个监测系统与其他类似的工作进行了比较，发现我们的系统可以实现更好的跌倒检测准确性和便携性。
