Determination of Fall Risk for Lower Limb Amputees
Abstract
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 andrefine 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 the data collection. We then performed our second experiment where we recruited 20 lower limb amputees to wear the sensor in the real-world. The data collection has been completed and we are currently refining our fall detection algorithm. An abstract describing our Aim 2 work was submitted and accepted for presentation at the 2023 annual meeting of the American Society of Biomechanics (August 8-11).
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2023
- Accession Number
- AD1228195
Entities
People
- Richard R Neptune
Organizations
- University of Texas at Austin