Fuzzy Logic and Machine Learning Algorithms for Detection and Classification of Falls and Activities of Daily Living
DOI:
https://doi.org/10.61467/2007.1558.2024.v15i4.497Keywords:
falls, Activities of Daily Living, accelerometer, gyroscope, fuzzy logic, machine learningAbstract
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.
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Copyright (c) 2024 International Journal of Combinatorial Optimization Problems and Informatics
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