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Video-Based Analysis of Heart Rate Applied to Falls

机译：基于视频的心率应用于跌倒的分析

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Falls are the number one cause of unintentional injuries and among the top-ten causes of deaths for older adults. An improved understanding of the causes of falls may lead to better approaches to prevention. Changes in blood pressure and heart rate can contribute to falls. Our goal is to develop techniques for measuring these parameters during real-life falls. In this paper, we apply advanced video processing techniques to footage of real-life falls in long-term care facilities to measure the heart rate of individuals before, during and after falls. We use an object-tracking algorithm to track a region of interest (ROI), such as the face, neck, hands or legs. We then estimate the heart rate based on subtle color changes in the ROI, which correlate with arterial blood flow. To verify the performance of the videobased method an experiment was conducted where the subject was wearing a pulse oximeter to measure the heart rate while being videotaped. Our results demonstrate that the proposed video-based processing method can be used to accurately capture heart rate variations during a fall. We also evaluate how the accuracy of the heart rate estimation depends on the size of the selected ROI. The proposed system could be used for remote patient monitoring and health informatics for improved detection and identification of the causes of falls.
机译：跌倒是造成意外伤害的第一大原因，也是老年人死亡的十大原因之一。对跌倒原因的更好理解可能会导致更好的预防方法。血压和心率的变化可能导致跌倒。我们的目标是开发在现实生活中跌落时测量这些参数的技术。在本文中，我们将先进的视频处理技术应用于长期护理设施中真实生活中的跌倒镜头，以测量跌倒前后的个人心率。我们使用对象跟踪算法来跟踪感兴趣的区域（ROI），例如脸，脖子，手或腿。然后，我们根据ROI的细微颜色变化（与动脉血流相关）来估算心率。为了验证基于视频的方法的性能，进行了一项实验，在该过程中，被摄对象戴着脉搏血氧仪以在录制视频时测量心率。我们的结果表明，所提出的基于视频的处理方法可用于准确捕获跌倒期间的心率变化。我们还评估了心率估计的准确性如何取决于所选ROI的大小。所提出的系统可以用于远程患者监测和健康信息学，以改进对跌倒原因的检测和识别。

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