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 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

• Autores: Ariza Colpas, Paola Patricia; VICARIO, ENRICO; De-La-Hoz-Franco, Emiro; Pineres-Melo, Marlon; Oviedo Carrascal, Ana Isabel; PATARA, FULVIO
• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/7356
• Acceso en línea: https://hdl.handle.net/11323/7356; https://doi.org/10.3390/s20092702; https://repositorio.cuc.edu.co/
• Palabra clave: ambient assisted living—AAL; human activity recognition—HAR; activities of dailyliving—ADL; ctivity recognition systems—ARS; clustering; unsupervised activity recognition
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International