Fuzzy Logic and Machine Learning Algorithms for Detection and Classification of Falls and Activities of Daily Living

Authors

  • Edmundo Bonilla Huerta TecNM
  • Eduardo Martínez Juárez TecNM
  • Roberto Morales Caporal TecNM
  • Eduardo Vázquez Urbina TecNM

DOI:

https://doi.org/10.61467/2007.1558.2024.v15i4.497

Keywords:

falls, Activities of Daily Living, accelerometer, gyroscope, fuzzy logic, machine learning

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.

 

Author Biographies

Edmundo Bonilla Huerta, TecNM

Departamento de Computación y Sistemas.

 

Roberto Morales Caporal, TecNM

Instituto Tecnológico de Apizaco · Department of Electrical and Electronic Engineering Dr.-Ing.

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Published

2024-11-04

How to Cite

Bonilla Huerta, E., Martínez Juárez, E., Morales Caporal, R., & Vázquez Urbina, E. (2024). Fuzzy Logic and Machine Learning Algorithms for Detection and Classification of Falls and Activities of Daily Living . International Journal of Combinatorial Optimization Problems and Informatics, 15(4), 42–60. https://doi.org/10.61467/2007.1558.2024.v15i4.497

Issue

Section

COMIA