Nowadays, companies are demanding better‐prepared professionals to succeed in a digital society, and the acquisition of Science, Technology, Engineering, Arts, and Mathematics (STEAM)‐related competencies is a key issue. One of the main problems in this sense is how to integrate STEAM into the current educational curricula. This is not something related to a subject or educational trend but rather to new methodological approaches that can engage students. In this sense, such active methodologies that apply mechatronics and robotics could be an interesting path to pursue. Given this context, the first necessary task in evaluating the potential of this approach is to understand the landscape of the application of robotics and mechatronics in STEAM Education and how active methodologies are applied in this sense. To carry out this analysis in a systematic and replicable manner, it is necessary to follow a methodology. In this case, the researchers employ a systematic mapping review. This paper presents this process and its main findings. Fifty‐four studies have been selected out of 242 total studies analyzed. From these, beyond obtaining a clear vision of the STEAM landscape regarding project topics, we can also conclude that robotics and physical devices have been applied successfully with collaborative methodologies in STEAM Education. Regarding conclusions, this paper shows that robotics and mechatronics applied with active methodologies is to be a good means to engage students in STEAM disciplines and thus aid the acquisition of what is commonly known as “21st‐century skills.”
STEAM Education is nowadays a key element for our current digital society. Integrating STEAM and developing competences such as Computational Thinking is highly demanded by the industry and higher education institutions. In order to do so new methodological approaches are required. RoboSTEAM project is an Erasmus+ project defined to address these topics by using of physical devices and robotics employing Challenge Based Learning methodology. One of the first steps in the project development is the definition of current landscape in the research field. Which means to carry out a literature mapping that considers previous applications of Challenge Based Learning in STEAM education and use of robots and physical devices to do so. This paper shows the mapping review process and the main results obtained. The mapping analyze 242 candidate works from the most relevant bibliographic sources and selected 54. Form them it was possible to see that there are not many initiatives on STEM Education related to Challenge base learning and the most of them are specially focused on the application of specific tools and in the development of concrete competences.
The use of people recognition techniques has become critical in some areas. For instance, social or assistive robots carry out collaborative tasks in the robotics field. A robot must know who to work with to deal with such tasks. Using biometric patterns may replace identification cards or codes on access control to critical infrastructures. The usage of Red Green Blue Depth (RGBD) cameras is ubiquitous to solve people recognition. However, this sensor has some constraints, such as they demand high computational capabilities, require the users to face the sensor, or do not regard users’ privacy. Furthermore, in the COVID-19 pandemic, masks hide a significant portion of the face. In this work, we present BRITTANY, a biometric recognition tool through gait analysis using Laser Imaging Detection and Ranging (LIDAR) data and a Convolutional Neural Network (CNN). A Proof of Concept (PoC) has been carried out in an indoor environment with five users to evaluate BRITTANY. A new CNN architecture is presented, allowing the classification of aggregated occupancy maps that represent the people’s gait. This new architecture has been compared with LeNet-5 and AlexNet through the same datasets. The final system reports an accuracy of 88%.
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