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\begin{document}

\title{Analysis of Waist and Wrist Positioning Wearable Machine Learning Models
to Detect Falls}



\author[1]{Teddy Ordoñez Nuñez}%
\author[1]{Alejandro Ramirez}%
\author[2]{Liliam Becherán Marón}%
\affil[1]{UNIVALI}%
\affil[2]{Universidad de la Habana}%


\vspace{-1em}



  
  \date{May 28, 2024}


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\begin{abstract}
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\%.%
\end{abstract}%



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