Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-05, Vol.20 (9), p.2702
• Main Authors: Ariza Colpas, Paola, Vicario, Enrico, De-La-Hoz-Franco, Emiro, Pineres-Melo, Marlon, Oviedo-Carrascal, Ana, Patara, Fulvio
• Format: Article
• Language: English
• Subjects: Activities of Daily Living; activities of daily living—ADL; activity recognition systems—ARS; Algorithms; ambient assisted living—AAL; Cluster Analysis; Clustering; Datasets; Human activity recognition; human activity recognition—HAR; Humans; Internet of Things; Knowledge; Literature reviews; Machine learning; Moving object recognition; Quality of Life; Review; Scientometrics; Sensors; Software development; Taxonomy; unsupervised activity recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20092702

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.