TY - JOUR
T1 - Human Activity Recognition Data Analysis
T2 - History, Evolutions, and New Trends
AU - Ariza-Colpas, Paola Patricia
AU - Vicario, Enrico
AU - Oviedo-Carrascal, Ana Isabel
AU - Aziz, Shariq Butt
AU - Piñeres-Melo, Marlon Alberto
AU - Quintero-Linero, Alejandra
AU - Patara, Fulvio
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
KW - activities of daily living—ADL
KW - activity recognition systems—ARS
KW - ambient assisted living—AAL
KW - clustering
KW - deep learning
KW - ensemble learning
KW - human activity recognition—HAR
KW - reinforcement learning
KW - supervised learning
KW - unsupervised activity recognition
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85129013868&partnerID=8YFLogxK
U2 - 10.3390/s22093401
DO - 10.3390/s22093401
M3 - Artículo de revisión
C2 - 35591091
AN - SCOPUS:85129013868
SN - 1424-8220
VL - 22
JO - Sensors
JF - Sensors
IS - 9
M1 - 3401
ER -