Fuzzy Logic and Machine Learning Algorithms for Detection and Classification of Falls and Activities of Daily Living
Edmundo Bonilla Huerta, Eduardo Martínez Juárez, Roberto Morales Caporal, Eduardo Vázquez Urbina · International Journal of Combinatorial Optimization Problems and Informatics. · 2024
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.