Moya-Albor, Ernesto
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Moya-Albor, Ernesto
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Moya Albor, Ernesto
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emoya
Moya-Albor, E.
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55523223700
Researcher ID
J-1891-2019

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From Project-Based Learning to Innovative Technologies in Mechatronics Course: A Case Study in a Private University in Mexico City

2024-01-01 , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Mechatronics engineering is a challenging discipline that needs different thinking and practices in contrast with traditional engineering. This challenging is mainly due to the demand of integration, collaboration and holistic approaches required during the design methodology. This study examines the transformation from traditional education to an in-deep professional and research focused projects. The key factor in the mechatronics learning practice includes the implementation of a major project focused on positive social impact solutions and the road map developed for this purpose. This work proposes a methodology that allows students develop a major project with a holistic view, including design constraints related to specific contextual aspects as economics, environmental, societal, ethical, health and sustainability. Also, students are able to develop professional and research skills. The methodology also allows students propose a major project focused on positive social impact with design constraints. It also exposes students different engineering and computational tools for collaboration and integration. The study uses data from 69 students enrolled along four years, from 2016 to 2019. Results show that the student learning outcomes increased significantly at the end of the period time, from to (in range between 0 to 4), reaching the satisfactory level (year-2016 as baseline). Also, 100% of the scientific papers derived from the major projects were accepted for publication in international conferences © 2024 Springer Nature

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Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review

2024-01-01 , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram , Escobedo-Gordillo, Andrés

Nowadays, the healthcare is a priority for both governments and persons. Vital sign monitoring allows knowing the health status and is widely used for prevention, diagnosis, and treatment of determined illnesses. In particular, breathing and heart rate are traditionally considered the most relevant and accessible vital signs. However, oxygen saturation was essential in the COVID-19 pandemic. On the other hand, contact techniques to estimate these vital signs are a standard monitoring reference. However, non-contact estimation methods have gained relevance in the last few years in those cases where there is the possibility of suffering stress, pain, and skin irritation in specific situations, as in the case of vulnerable skin in burn patients and neonates. In this chapter, a review of contactless video-based vital-sign methods is presented. The selected methods have a data-driven approach as an alternative when there is not theoretical model of the physiological phenomenon. Finally, a new framework with a general data-driven approach to estimate the most used vital signs is proposed. This framework includes a region of interest extraction stage, a video magnification technique to reveals subtle changes, and a machine learning method to estimate the vital signs. In addition, each step describes some recommendations and best practices found ©Springer.

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