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Background:  To begin research with cross-thematic topics, an analysis is necessary that collects data and historical accounts about these topics. In the field of health and technology, this principle is not unknown. In this case, it is the interest of this investigation to inquire about the topics "wearable" and "health" in order to gather enough information to establish how it has been developed, what the current topics are and what areas it is planned to study in a near future. The purpose of this study is to analyse the growth of the wearable technology and health subject matter. This will create an account of the most relevant themes over time in this subject and their evolution. This study will have an emphasis on recent years, in order to see which themes may be important for future research and publications. Methods: This paper aims at making a bibliometric exploration using information from the Scopus database using the search chain “weareable AND health” within the years of 2000 to 2017. The bibliometric software SciMAT was used for data visualization and analysis. Results: The results obtained are an analysis of the growth rate that is presented in Scopus publications between 2000 and 2017. In addition, a bibliometric analysis is obtained through the SciMat software, which shows the relevant issues that arise through time and that may be relevant for future research. Conclusions: Publications associated with the search chain “Wearable AND Health” from 2000 to 2017 were generally in constant growth over the years. With the SciMAT software, the main theme that was found was that of monitoring systems. In addition, it shows how new themes that are highly relevant emerge, such as mobile technologies and the study of algorithms, which emerge as an evolution from signal processing.

In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of. This article proposes an algorithm in which two periodic movements are compared, the one carried out by an expert and the one carried out by the person who is learning, in order to determine how closely these two movements are performed and to provide feedback from them. The algorithm starts from the capture of data through a wearable device that yields data from an accelerometer; in this case, the data of the expert and the data of the person who is learning are captured in a dataset of salsa dance steps. Adjustments are made to the data in terms of Pearson iterations, synchronization, filtering, and normalization, and DTW, linear regression, and error analysis are used to make the corresponding comparison of the two datasets. With the above, it is possible to determine if the cycles of the two signals coincide and how closely the learner’s movements resemble those of the expert.