Predictive Model for Human Activity Recognition Based on 2 Machine Learning and Feature Selection Techniques
Fecha
2022-09-27
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MDPI
Resumen
Research into assisted living environments –within the area of Ambient Assisted Living (ALL)– fo- 18 cuses on generating innovative technology, products, and services to provide medical treatment and rehabil- 19 itation to the elderly, with the purpose of increasing the time in which these people can live independently, 20 whether or not they suffer from neurodegenerative diseases or disabilities. This key area is responsible for 21 the development of activity recognition systems (ARS) which are a valuable tool to identify the types of ac- 22 tivities carried out by the elderly, in order to provide them with effective care that allows them to carry out 23 daily activities normally. This article aims to review the literature in order to outline the evolution of the 24 different data mining techniques applied to this health area, by showing the metrics used by researchers in 25 this area of knowledge in recent experiments.
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Palabras clave
Human activity recognition (HAR), Machine learning, Classification, Feature selection
Citación
Patiño-Saucedo, J. A., Ariza-Colpas, P. P., Butt-Aziz, S., Piñeres-Melo, M. A., López-Ruiz, J. L., Morales-Ortega, R. C., & De-la-hoz-Franco, E. (2022). Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques. International Journal of Environmental Research and Public Health, 19(19), 12272. https://doi.org/10.3390/ijerph191912272