Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques.
Janns Alvaro Patiño-SaucedoPaola Patricia Ariza-ColpasShariq Aziz ButtMarlon Alberto Piñeres-MeloJosé Luis López-RuizRoberto Cesar Morales-OrtegaEmiro De-La-Hoz-FrancoPublished in: International journal of environmental research and public health (2022)
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.
Keyphrases
- healthcare
- machine learning
- middle aged
- endothelial cells
- big data
- systematic review
- public health
- community dwelling
- air pollution
- particulate matter
- primary care
- deep learning
- palliative care
- quality improvement
- artificial intelligence
- health information
- affordable care act
- physical activity
- induced pluripotent stem cells
- chronic pain
- combination therapy
- data analysis
- health promotion