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A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

机译:一种数据驱动的监测技术,用于增强跌倒事件检测

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Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.
机译:跌倒检测是老年人保健中的关键问题。在这项工作中,我们提出了一种通过简单的人体描述符检测跌倒的创新方法。所提取的特征具有足够的判别力来描述人体姿势,并且在计算上不太复杂以至于不能进行快速处理。跌倒检测被解决为统计异常检测问题。所提出的方法将使用主成分分析模型的建模与指数加权移动平均值(EWMA)监视图结合起来。 EWMA方案应用于忽略的主成分,以检测跌倒的存在。使用两个不同的跌倒检测数据集URFD和FDD,我们已经证明了该开发方法比传统的基于PCA的方法具有更高的灵敏度和有效性。

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