Abstract
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.
Highlights
Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors [73]
Taking into account the fact that clustering is one of the techniques mostly used in issues of extraction and the selection of similarities and common characteristics between objects, different architectures or forms of implementation have been developed that allow its correct application in the recognition of activities of daily life
Among the most relevant technical aspects of the meta-analytical matrix is the identification of the clustering algorithm and the different analyses of the quality of the techniques associated with the different experiments
Summary
Clustering, called grouping, aims to divide the data into groups of objects with similar characteristics Through this technique, simplification of data information is achieved. Extraction or Selection of Feature: In this phase, it is necessary to define the characteristics or similarities to be analyzed. Clustering Algorithm selection: After extracting the feature, it is necessary to define the clustering algorithm to be applied In addition to this important selection, defining a corresponding proximity measure and the construction of a criterion function is indispensable. The big difficulty is to understand and know the quality of the results—the results are defined by the clustering quality metrics [6] These metrics are divided into two groups: externals and internal. Phase 4: Result Interpretation: The purpose of using clustering is to show new information extracted from the original data to solve the initial problem. Additional experiments can be applied in order to explain and prove the extracted knowledge
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