Fuzzy Logic and Machine Learning Algorithms for Detection and Classification of Falls and Activities of Daily Living
Year: 2024
Type: article
Abstract: This article analyses the movements of young and elderly people using data collected from an accelerometer and a gyroscope. This study proposes Type I fuzzy logic (FL) and several machine learning (ML) algorithms for the detection and classification of daily life movements and falls. The results obtained demonstrate that a fuzzy logic system can efficiently integrate data from an accelerometer and a gyroscope to classify falls and movements in daily life with 97.4% accuracy. When ML classifiers are used, the performance across several algorithms is also very high.
Authors: Edmundo Bonilla Huerta, Eduardo Martínez Juárez, Roberto Morales Caporal, Eduardo Vázquez Urbina
Institution: Tecnológico Nacional de México
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404039579