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Automated fall risk assessment of elderly using wearable devices.

Marian HaescherWencke ChodanFlorian HöpfnerGerald BieberMario AehneltKarthik SrinivasanMargit Alt Murphy
Published in: Journal of rehabilitation and assistive technologies engineering (2020)
The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.
Keyphrases
  • risk assessment
  • machine learning
  • deep learning
  • human health
  • high throughput
  • endothelial cells
  • heart rate
  • heavy metals
  • blood pressure
  • emergency department
  • cross sectional