loading page

Analysis of Waist and Wrist Positioning Wearable Machine Learning Models to Detect Falls
  • Teddy Ordoñez Nuñez,
  • Alejandro Ramirez,
  • Liliam Becherán Marón
Teddy Ordoñez Nuñez
Author Profile
Alejandro Ramirez
Author Profile
Liliam Becherán Marón
Universidad de la Habana

Corresponding Author:[email protected]

Author Profile


Falls are a widespread issue affecting people worldwide, regardless of their social status. Falls lead to physical, psychological, and economic consequences. Experts are developing solutions for this problem, given the high frequency of falls among the elderly. This study presents various ML models, which can predict human falls using signals of a wearable sensor located on the wrist or the waist. By extracting the mean, standard deviation, and range, we were able to train and evaluate various machine learning models considering accelerometers and gyroscopes as sensors. The combination of these characteristics and sensors resulted in the RF waist model achieving the most favorable metrics, achieving an accuracy rate of 97.22%.