Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of l...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/9/2702 |
| _version_ | 1797568475722416128 |
|---|---|
| author | Paola Ariza Colpas Enrico Vicario Emiro De-La-Hoz-Franco Marlon Pineres-Melo Ana Oviedo-Carrascal Fulvio Patara |
| author_facet | Paola Ariza Colpas Enrico Vicario Emiro De-La-Hoz-Franco Marlon Pineres-Melo Ana Oviedo-Carrascal Fulvio Patara |
| author_sort | Paola Ariza Colpas |
| collection | DOAJ |
| description | Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge. |
| first_indexed | 2024-03-10T19:56:25Z |
| format | Article |
| id | doaj.art-698990d4880644298efed070435f7282 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:56:25Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-698990d4880644298efed070435f72822023-11-19T23:54:22ZengMDPI AGSensors1424-82202020-05-01209270210.3390/s20092702Unsupervised Human Activity Recognition Using the Clustering Approach: A ReviewPaola Ariza Colpas0Enrico Vicario1Emiro De-La-Hoz-Franco2Marlon Pineres-Melo3Ana Oviedo-Carrascal4Fulvio Patara5Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, ColombiaDepartment of Information Engineering, University of Florence, 50139 Firenze, ItalyDepartment of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, ColombiaDepartment of Systems Engineering, Universidad del Norte, Barranquilla 081001, ColombiaFaculty of Engineering in Information and Communication Technologies, Universidad Pontificia Bolivariana, Medellín 050031, ColombiaDepartment of Information Engineering, University of Florence, 50139 Firenze, ItalyCurrently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.https://www.mdpi.com/1424-8220/20/9/2702ambient assisted living—AALhuman activity recognition—HARactivities of daily living—ADLactivity recognition systems—ARSclusteringunsupervised activity recognition |
| spellingShingle | Paola Ariza Colpas Enrico Vicario Emiro De-La-Hoz-Franco Marlon Pineres-Melo Ana Oviedo-Carrascal Fulvio Patara Unsupervised Human Activity Recognition Using the Clustering Approach: A Review Sensors ambient assisted living—AAL human activity recognition—HAR activities of daily living—ADL activity recognition systems—ARS clustering unsupervised activity recognition |
| title | Unsupervised Human Activity Recognition Using the Clustering Approach: A Review |
| title_full | Unsupervised Human Activity Recognition Using the Clustering Approach: A Review |
| title_fullStr | Unsupervised Human Activity Recognition Using the Clustering Approach: A Review |
| title_full_unstemmed | Unsupervised Human Activity Recognition Using the Clustering Approach: A Review |
| title_short | Unsupervised Human Activity Recognition Using the Clustering Approach: A Review |
| title_sort | unsupervised human activity recognition using the clustering approach a review |
| topic | ambient assisted living—AAL human activity recognition—HAR activities of daily living—ADL activity recognition systems—ARS clustering unsupervised activity recognition |
| url | https://www.mdpi.com/1424-8220/20/9/2702 |
| work_keys_str_mv | AT paolaarizacolpas unsupervisedhumanactivityrecognitionusingtheclusteringapproachareview AT enricovicario unsupervisedhumanactivityrecognitionusingtheclusteringapproachareview AT emirodelahozfranco unsupervisedhumanactivityrecognitionusingtheclusteringapproachareview AT marlonpineresmelo unsupervisedhumanactivityrecognitionusingtheclusteringapproachareview AT anaoviedocarrascal unsupervisedhumanactivityrecognitionusingtheclusteringapproachareview AT fulviopatara unsupervisedhumanactivityrecognitionusingtheclusteringapproachareview |