Artículo de revista
Human activity recognition data analysis: history, evolutions, and new trends
Registro en:
1424-3210
10.3390/s22093401
1424-8220
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
Autor
Ariza Colpas, Paola Patricia
sVicario, Enrico
Oviedo Carrascal, Ana Isabel
aziz, shariq
Piñeres Melo, Marlon Alberto
Quintero linero, Alejandra paola
PATARA, FULVIO
Institución
Resumen
The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.
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