Context: In recent years, the recognition of human activities has become an area of constant exploration in different fields. This article presents a literature review focused on the different types of human activities and information acquisition devices for the recognition of activities. It also delves into elderly fall detection via computer vision using feature extraction methods and artificial intelligence techniques. Methodology: This manuscript was elaborated following the criteria of the document review and analysis methodology (RAD), dividing the research process into the heuristics and hermeneutics of the information sources. Finally, 102 research works were referenced, which made it possible to provide information on current state of the recognition of human activities. Results: The analysis of the proposed techniques for the recognition of human activities shows the importance of efficient fall detection. Although it is true that, at present, positive results are obtained with the techniques described in this article, their study environments are controlled, which does not contribute to the real advancement of research. Conclusions: It would be of great impact to present the results of studies in environments similar to reality, which is why it is essential to focus research on the development of databases with real falls of adults or in uncontrolled environments.

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ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍAEste artículo presenta la formalización matemática de las redes de Petri híbridas y coloreadas para el anidamiento Latente de Fallos (AL), como método de abstracción respecto al diagnóstico de fallos llamado Redes de Petri Híbridas y Coloreadas para el Diagnóstico de Fallos (RdPHCDF). Además de esto, se demuestra la mayor capacidad de síntesis y modelado en el aislamiento y diagnóstico de fallos en sistemas complejos. Por otra parte, este trabajo presenta un ejemplo práctico de aplicación del método que consiste en un tanque de almacenamiento de líquidos.

The implementation of digital manufacturing technologies (DMTs) represents the beginning of transforming a manufacturing system towards a smart manufacturing system (SMS). Assessing the performance of the DMTs implemented is essential to meet the objectives in a SMS and allows identifying their usefulness. However, estimating this performance is a challenging task due to the heterogeneous characteristics of the DMTs, such as the origin of information, capacity, connectivity, etc. Although some SMS performance measurement metrics are known, none are intended to identify the performance of DMTs. This article follows a methodology for the construction of technological performance indicators and proposes a novel indicator based on the individual characteristics of the DMTs and the smart factory concept of interoperability. The proposed indicator allows approaching the behavior of one or multiple DMTs implemented simultaneously and introduces a quantifiable measurement that can be applied to any industrial process. It is noteworthy, that such an indicator is not present in the literature and may be of great interest to enterprises currently implementing DMTs related to SMS. The applicability of the indicator considering multiple DMTs is validated through an illustrative test case.

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