Oner Melis, Pulcifer-Stump Jeffry A, Seeling Patrick, Kaya Tolga
School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI 48859, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1980-3. doi: 10.1109/EMBC.2012.6346344.
Falling is one of the most common accidents with potentially irreversible consequences, especially considering special groups, such as the elderly or disabled. One approach to solve this issue would be an early detection of the falling event. Towards reaching the goal of early fall detection, we have worked on distinguishing and monitoring some basic human activities such as walking and running. Since we plan to implement the system mostly for seniors and the disabled, simplicity of the usage becomes very important. We have successfully implemented an algorithm that would not require the acceleration sensor to be fixed in a specific position (the smart phone itself in our application), whereas most of the previous research dictates the sensor to be fixed in a certain direction. This algorithm reviews data from the accelerometer to determine if a user has taken a step or not and keeps track of the total amount of steps. After testing, the algorithm was more accurate than a commercial pedometer in terms of comparing outputs to the actual number of steps taken by the user.
跌倒属于最常见的意外事故之一，可能会造成不可逆转的后果，尤其是对于老年人或残疾人等特殊群体而言。解决这一问题的一种方法是对跌倒事件进行早期检测。为了实现早期跌倒检测的目标，我们致力于区分和监测一些基本的人类活动，如行走和跑步。由于我们计划主要为老年人和残疾人实施该系统，因此使用的简便性变得非常重要。我们成功实现了一种算法，该算法不需要将加速度传感器固定在特定位置（在我们的应用程序中是智能手机本身），而大多数先前的研究要求将传感器固定在特定方向。该算法会查看来自加速度计的数据，以确定用户是否迈出了一步，并记录总步数。经过测试，在将输出与用户实际步数进行比较时，该算法比商用计步器更准确。
