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Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques

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  • Janns Alvaro Patiño-Saucedo

    (Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia)

  • Paola Patricia Ariza-Colpas

    (Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia)

  • Shariq Butt-Aziz

    (Department of Computer Science and IT, University of Lahore, Lahore 44000, Pakistan)

  • Marlon Alberto Piñeres-Melo

    (Department of Systems Engineering, Universidad del Norte, Barranquilla 081001, Colombia)

  • José Luis López-Ruiz

    (Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain)

  • Roberto Cesar Morales-Ortega

    (Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia)

  • Emiro De-la-hoz-Franco

    (Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia)

Abstract

Research into assisted living environments –within the area of Ambient Assisted Living (ALL)—focuses on generating innovative technology, products, and services to provide medical treatment and rehabilitation to the elderly, with the purpose of increasing the time in which these people can live independently, whether they suffer from neurodegenerative diseases or disabilities. This key area is responsible for the development of activity recognition systems (ARS) which are a valuable tool to identify the types of activities carried out by the elderly, and to provide them with effective care that allows them to carry out daily activities normally. This article aims to review the literature to outline the evolution of the different data mining techniques applied to this health area, by showing the metrics used by researchers in this area of knowledge in recent experiments.

Suggested Citation

  • Janns Alvaro Patiño-Saucedo & Paola Patricia Ariza-Colpas & Shariq Butt-Aziz & Marlon Alberto Piñeres-Melo & José Luis López-Ruiz & Roberto Cesar Morales-Ortega & Emiro De-la-hoz-Franco, 2022. "Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12272-:d:927062
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    References listed on IDEAS

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    1. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
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