Next Article in Journal
Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons
Next Article in Special Issue
A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes
Previous Article in Journal
Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones
Previous Article in Special Issue
Legodroid: A Type-Driven Library for Android and LEGO Mindstorms Interoperability
Open AccessReview

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

1
Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia
2
Department of Information Engineering, University of Florence, 50139 Firenze, Italy
3
Department of Systems Engineering, Universidad del Norte, Barranquilla 081001, Colombia
4
Faculty of Engineering in Information and Communication Technologies, Universidad Pontificia Bolivariana, Medellín 050031, Colombia
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(9), 2702; https://doi.org/10.3390/s20092702
Received: 20 January 2020 / Revised: 13 April 2020 / Accepted: 21 April 2020 / Published: 9 May 2020
(This article belongs to the Special Issue Human-Machine Interaction and Sensors)
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. View Full-Text
Keywords: ambient assisted living—AAL; human activity recognition—HAR; activities of daily living—ADL; activity recognition systems—ARS; clustering; unsupervised activity recognition ambient assisted living—AAL; human activity recognition—HAR; activities of daily living—ADL; activity recognition systems—ARS; clustering; unsupervised activity recognition
Show Figures

Figure 1

MDPI and ACS Style

Ariza Colpas, P.; Vicario, E.; De-La-Hoz-Franco, E.; Pineres-Melo, M.; Oviedo-Carrascal, A.; Patara, F. Unsupervised Human Activity Recognition Using the Clustering Approach: A Review. Sensors 2020, 20, 2702.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop