Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life...

ver descrição completa

Detalhes bibliográficos
Autores: Ariza Colpas, Paola Patricia, VICARIO, ENRICO, De-La-Hoz-Franco, Emiro, Pineres-Melo, Marlon, Oviedo Carrascal, Ana Isabel, PATARA, FULVIO
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:Colombia
Recursos:Corporación Universidad de la Costa
Repositorio:Repositorio REDICUC
Idioma:inglés
OAI Identifier:oai:repositorio.cuc.edu.co:11323/7356
Acesso em linha:https://hdl.handle.net/11323/7356
https://doi.org/10.3390/s20092702
https://repositorio.cuc.edu.co/
Access Level:acceso abierto
Palavra-chave:ambient assisted living—AAL
human activity recognition—HAR
activities of dailyliving—ADL
ctivity recognition systems—ARS
clustering
unsupervised activity recognition
Descrição
Resumo:Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledge