CareFall: Automatic Fall Detection through Wearable Devices and AI
  Methods
Juan Carlos Ruiz-Garcia Ruben Tolosana Ruben Vera-Rodriguez Carlos Moro
The aging population has led to a growing number of falls in our society,
affecting global public health worldwide. This paper presents CareFall, an
automatic Fall Detection System (FDS) based on wearable devices and Artificial
Intelligence (AI) methods. CareFall considers the accelerometer and gyroscope
time signals extracted from a smartwatch. Two different approaches are used for
feature extraction and classification: i) threshold-based, and ii) machine
learning-based. Experimental results on two public databases show that the
machine learning-based approach, which combines accelerometer and gyroscope
information, outperforms the threshold-based approach in terms of accuracy,
sensitivity, and specificity. This research contributes to the design of smart
and user-friendly solutions to mitigate the negative consequences of falls
among older people.