CareFall: Automatic Fall Detection through Wearable Devices and AI Methods

TL;DR

CareFall is an AI-powered fall detection system utilizing wearable smartwatch sensors, demonstrating improved accuracy over threshold methods, aimed at reducing fall-related health risks in the elderly.

Contribution

Introduces a novel AI-based fall detection system using wearable sensors, outperforming traditional threshold methods in accuracy and reliability.

Findings

Machine learning approach outperforms threshold-based method

Combining accelerometer and gyroscope data improves detection

System shows high accuracy, sensitivity, and specificity

Abstract

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.