Abstract
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.
| Original language | English |
|---|---|
| Article number | 3401 |
| Journal | Sensors |
| Volume | 22 |
| Issue number | 9 |
| DOIs | |
| State | Published - 1 May 2022 |
Bibliographical note
Publisher Copyright:© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
- activities of daily living—ADL
- activity recognition systems—ARS
- ambient assisted living—AAL
- clustering
- deep learning
- ensemble learning
- human activity recognition—HAR
- reinforcement learning
- supervised learning
- unsupervised activity recognition
- unsupervised learning
Types Minciencias
- Artículos de investigación con calidad A1 / Q1
