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 were 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 performed two human subject experiments. The first experiment used 30 non-amputee and 5 lower limb amputee individuals to simulate falls in a laboratory setting while wearing IMU sensors. Due to the COVID-19 pandemic, we were delayed in starting the data collection. However, in January 2021we were given approval to start data collection, which was completed along with the development of the fall detection algorithm (Aim 1). We then performed our second experiment where we recruited 20 lower limb amputees to wear the sensor in the real-world. The data collection was completed and the fall detection algorithm was further refined (Aim 2). This research resulted in three conference presentations and two journal manuscripts.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2024
- Accession Number
- AD1228214
Entities
People
- Richard R Neptune
Organizations
- University of Texas at Austin