Aziz Omar, Russell Colin M, Park Edward J, Robinovitch Stephen N
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:30-3. doi: 10.1109/EMBC.2014.6943521.
Falls are a major cause of death and morbidity in older adults. In recent years many researchers have examined the role of wearable inertial sensors (accelerometers and/or gyroscopes) to automatically detect falls. The primary goal of such fall monitors is to alert care providers of the fall event, who can then commence earlier treatment. Although such fall detection systems may reduce time until the arrival of medical assistance, they cannot help to prevent or reduce the severity of traumatic injury caused by the fall. In the current study, we extend the application of wearable inertial sensors beyond post-impact fall detection, by developing and evaluating the accuracy of a sensor system for detecting falls prior to the fall impact. We used support vector machine (SVM) analysis to classify 7 fall and 8 non-fall events. In particular, we focused on the effect of data window size and lead time on the accuracy of our pre-impact fall detection system using signals from a single waist sensor. We found that our system was able to detect fall events at between 0.0625-0.1875 s prior to the impact with at least 95% sensitivity and at least 90% specificity for window sizes between 0.125-1 s.
跌倒是老年人死亡和发病的主要原因。近年来，许多研究人员研究了可穿戴惯性传感器（加速度计和/或陀螺仪）在自动检测跌倒方面的作用。此类跌倒监测器的主要目标是提醒护理人员注意跌倒事件，以便他们能够尽早开始治疗。尽管此类跌倒检测系统可能会减少获得医疗援助的时间，但它们无法帮助预防或减轻跌倒造成的创伤性损伤的严重程度。在当前的研究中，我们通过开发和评估一种用于在跌倒撞击前检测跌倒的传感器系统的准确性，将可穿戴惯性传感器的应用扩展到撞击后跌倒检测之外。我们使用支持向量机（SVM）分析对7次跌倒和8次非跌倒事件进行分类。特别是，我们关注数据窗口大小和提前时间对使用来自单个腰部传感器的信号的撞击前跌倒检测系统准确性的影响。我们发现，对于0.125 - 1秒之间的窗口大小，我们的系统能够在撞击前0.0625 - 0.1875秒检测到跌倒事件，灵敏度至少为95%，特异性至少为90%。
