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Determination of Fall Risk for Lower Limb Amputees

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[Technical Report, Annual Report]

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Falling is a common problem for lower limb amputees, which can lead to reduced physical and emotional health. The overall aims of this project are to 1 establish a baseline fall detection algorithm derived from simulated falls in a laboratory setting, and 2 utilize and refine the initial laboratory-based algorithm to provide detection of fall events during activities of daily living in real-world environments. To achieve these aims we will perform two human subject experiments. The first experiment will use 30 non-amputee and 5 lower limb amputee individuals to simulate falls in a laboratory setting while wearing the sensor. However, due to the COVID-19 pandemic, we were delayed in starting our data collection. However, in January 2021 we were given approval to start data collection and we have completed 30 non-amputee and 4 lower limb amputee individuals to date. We are currently refining our baseline fall detection algorithm and will begin implementing the algorithm in the sensor the amputees will wear in our second experiment where we will recruit 40 lower limb amputees to wear the sensor in the real-world and we will further refine the algorithm. This will be the focus on Year 2 of the project. An abstract describing our preliminary work was submitted and accepted for presentation at the annual meeting of the American Society of Biomechanics.

Subject Categories:

  • Medicine and Medical Research
  • Anatomy and Physiology

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[A, Approved For Public Release]