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RTLA-HAR: A model proposal based on Reinforcement and Transfer Learning for the Adaptation of learning in Human Activity Recognition.

Paola Patricia Ariza-Colpas, Ana Isabel Oviedo-Carrascal, Shariq Aziz Butt, Marlon Alberto Piñeres-Melo


The Assisted Living Environment Research Area – AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist medical attention and rehabilitation to the elderly, with the purpose of increasing the time in which these people can live independently, since whether or not they suffer from neurodegenerative diseases or a disability. This important area is responsible for the development of systems for the recognition of activity - ARS (Activity Recognition Systems) which are a valuable tool when identifying the type of activity carried out by the elderly, in order to provide them with effective assistance that allows you to carry out daily activities with total normality. This article aims to show the review of the literature and the evolution of the different data mining techniques applied to this health sector, by showing the metrics of recent experiments for researchers in this area of knowledge. The objective of this article is to carry out the review of highly relevant research works in terms of learning based on reinforcement and transfer, to later outline the different components of the RTLHAR model, for the identification and adaptation of learning focused on the recognition of human activities.


Human Activity Recognition – HAR, Activities of Daily Living – ADL, Selection Techniques, Classification Techniques, Smart Home

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