Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia.
Department of Information Engineering, University of Florence, Firenze 50139, Italy.
Sensors (Basel). 2020 May 9;20(9):2702. 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.
目前，许多应用程序已经从软件开发和硬件使用的实施中出现，被称为物联网。这种技术的最重要的应用领域之一是在医疗保健领域。为了提高生活质量，并促进在家中患有不同疾病的患者的治疗得到改善，每天都会出现各种应用程序。这就是为什么出现了一条非常有兴趣的工作线，专注于研究和分析日常生活活动，使用不同的数据分析技术来识别和帮助管理这种类型的患者。本文展示了对文献中使用聚类方法的系统综述的结果，聚类方法是应用于日常生活活动的无监督数据分析中最常用的技术之一，以及描述高重要性变量的描述，如发布年份、文章类型、最常用的算法、使用的数据集类型和实现的指标。这些数据将使读者能够找到该技术在特定知识领域的应用的最新结果。
