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    Item type:Publication,
    Machine learning models in health prevention and promotion and labor productivity: A co-word analysis
    (Pro-Metrics, 2024)
    Domínguez Miranda, Sergio Arturo
    ;
    Rodríguez Aguilar, Román  
    Objective: The objective of this article is to carry out a co-word study on the application of machine learning models in health prevention and promotion, and its effect on labor productivity. Methodology: The analysis of the relevant literature on the proposed topic, identified in the last 15 years in Scopus, is considered. Articles, books, book chapters, editorials, conference papers and reviews refereed publications were considered. A thematic mapping analysis was performed using factor analysis and strategy diagrams to derive primary research approaches and identify frequent themes as well as thematic evolution. Results: The results of this study show the selection of 87 relevant publications with an average annual growth rate of 23.25% in related production. The main machine learning algorithms used, the main research approaches and key authors, derived from the analysis of thematic maps, were identified. Conclusions: This study emphasizes the importance of using co-word analysis to understand trends in research on the impact of health prevention and promotion on labor productivity. The potential benefits of using machine learning models to address this issue are highlighted and anticipated to guide future research focused on improvements in labor productivity through prevention and promotion of health. Originality: The identification of the relationship between work productivity and health prevention and promotion through machine learning models is a relevant topic but little analyzed in recent literature. The analysis of co-words allows us to establish the reference point of the state of the art in this regard and future trends.
    Scopus© Citations 1  20
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    Implementation of a SVM on an Embedded System: A Case Study on Fall Detection
    (2020)
    Márquez Ordaz, Luis Eusebio
    ;
    Ponce, Hiram  
    Edge Computing seeks to bring Machine Learning as close as possible to the source events of interest, providing an almost instant interpretation to data acquired by sensors giving sense to raw data while addressing concerns of particular applications such as latency, privacy and server stress relieve. Due to a lack of research on this particular type of application, we are faced with difficulties both in software and hardware as embedded systems are known to possess serious limitations on its available processing resources. To address this, we make use of the concepts of edge computing and offline programming to accomplish a reliable machine learning model deployment on the microprocessor. By studying real case problem, we can get measurements on the resources required by such an application as well as its performance. In this study, we address the implementation of such an application in an embedded system focusing on the detection of human falls. © 2020, Springer Nature Switzerland AG.
    Scopus© Citations 1  15  2