Stone Erik E, Skubic Marjorie
IEEE J Biomed Health Inform. 2015 Jan;19(1):290-301. doi: 10.1109/JBHI.2014.2312180. Epub 2014 Mar 17.
A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.
提出了一种使用微软Kinect和两阶段跌倒检测系统来检测老年人家庭中跌倒情况的方法。检测系统的第一阶段在各个深度图像帧中表征人的垂直状态，然后从通过随时间跟踪人而获得的垂直状态时间序列中分割出地面事件。第二阶段使用决策树集成来计算地面事件之前发生跌倒的置信度。在老年人的实际家中进行了评估，使用了在13套公寓中收集的长达九年的连续数据。该数据集包括454次跌倒、445次由训练有素的特技演员表演的跌倒以及9次自然发生的住户跌倒。广泛的数据收集使得能够在现实世界条件下对系统性能进行表征，其程度在其他研究中尚未有过展示。还包括了针对站立、坐着和躺下姿势、近距离（4米以内）与远距离跌倒位置以及有遮挡与无遮挡跌倒者的交叉验证结果。该方法与五种最先进的跌倒检测算法进行了比较，并取得了显著更好的结果。
