Video-Based Analysis of Heart Rate Applied to Falls

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 tec...

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Vydané v:2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) s. 1 - 5
Hlavní autori: He, Xiaochuan, Goubran, Rafik, Bennett, Stephanie, Robinovitch, Stephen, Symes, Bobbi, Lo, Bryan, Ejupi, Andreas, Knoefel, Frank
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Jazyk:English
Vydavateľské údaje: IEEE 01.06.2018
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Abstract 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.
AbstractList 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.
Author Ejupi, Andreas
Symes, Bobbi
He, Xiaochuan
Lo, Bryan
Goubran, Rafik
Bennett, Stephanie
Knoefel, Frank
Robinovitch, Stephen
Author_xml – sequence: 1
  givenname: Xiaochuan
  surname: He
  fullname: He, Xiaochuan
  organization: Department of Computer and Systems Engineering, Carleton University, Ottawa, Canada
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  givenname: Rafik
  surname: Goubran
  fullname: Goubran, Rafik
  organization: Department of Computer and Systems Engineering, Carleton University, Ottawa, Canada
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  givenname: Stephanie
  surname: Bennett
  fullname: Bennett, Stephanie
  organization: Department of Computer and Systems Engineering, Carleton University, Ottawa, Canada
– sequence: 4
  givenname: Stephen
  surname: Robinovitch
  fullname: Robinovitch, Stephen
  organization: Department of Biomedical Physiology and Kinesiology, Simon Fraser University
– sequence: 5
  givenname: Bobbi
  surname: Symes
  fullname: Symes, Bobbi
  organization: Department of Biomedical Physiology and Kinesiology, Simon Fraser University
– sequence: 6
  givenname: Bryan
  surname: Lo
  fullname: Lo, Bryan
  organization: Department of Biomedical Physiology and Kinesiology, Simon Fraser University
– sequence: 7
  givenname: Andreas
  surname: Ejupi
  fullname: Ejupi, Andreas
  organization: Department of Biomedical Physiology and Kinesiology, Simon Fraser University
– sequence: 8
  givenname: Frank
  surname: Knoefel
  fullname: Knoefel, Frank
  organization: Department of Computer and Systems Engineering, Carleton University, Ottawa, Canada
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Snippet 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...
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SubjectTerms Cameras
fall analysis
Heart rate
Object tracking
Pulse measurements
remote patient monitoring
Sociology
Statistics
Streaming media
video analysis
Title Video-Based Analysis of Heart Rate Applied to Falls
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