Soaz Cristina, Lederer Christian, Daumer Martin
SLCMSR e.V. - The Human Motion Institute, Munich, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:244-7. doi: 10.1109/EMBC.2012.6345915.
Falls are a major concern for the elderly and their ability to remain healthy. Fall detection systems may notify emergency responders when no one apart from the injured is present. However, their real-world application is limited by a number of factors such as high false positive rates, low-compliance, poor-usability and short battery lifetime. In order to improve these aspects we have developed a miniaturized 3D accelerometer integrated in a belt buckle, the actibelt(®), and a fall detection algorithm. We have used a new evaluation method to assess the upper limit of the false alarm rate of our algorithm using a large set of long term standardized acceleration measurements recorded under real life conditions. Our algorithm has a false alarm rate of seventeen false alarms per month and has the potential to be reduced down to at most three false alarms per month when activities which require the sensor to be removed are eliminated. In laboratory settings, the algorithm has a sensitivity of 100%. The algorithm was sucessfully validated using data from a real-world fall.
跌倒对于老年人及其保持健康的能力而言是一个主要问题。当除受伤者外没有其他人在场时，跌倒检测系统可以通知应急响应人员。然而，它们在现实世界中的应用受到许多因素的限制，如高误报率、低依从性、差的可用性以及短电池寿命。为了改善这些方面，我们开发了一种集成在腰带扣中的小型化3D加速度计——活动腰带（actibelt(®)）以及一种跌倒检测算法。我们使用了一种新的评估方法，利用在现实生活条件下记录的大量长期标准化加速度测量数据来评估我们算法误报率的上限。我们的算法每月有17次误报，当消除需要移除传感器的活动时，有可能将误报率降低到每月最多3次。在实验室环境中，该算法的灵敏度为100%。该算法已通过来自真实跌倒数据成功验证。
