Predictive model for human activity recognition based on machine learning and feature selection techniques

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.

• Autores: Patiño-Saucedo, Janns Alvaro; Ariza-Colpas, Paola Patricia; aziz, shariq; Piñeres-Melo, Marlon Alberto; López Ruiz, José Luis; Morales-Ortega, Roberto Cesar; De-La-Hoz-Franco, Emiro
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2022
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/10880
• Acceso en línea: https://hdl.handle.net/11323/10880; https://repositorio.cuc.edu.co/
• Palabra clave: Human activity recognition (HAR); Machine learning; Classification; Feature selection
• Rights: openAccess
• License: Atribución 4.0 Internacional (CC BY 4.0)