Abstract: Embodied embedded cognition (EEC) has gained support in cognitive science as well as in human–computer interaction (HCI). EEC can be characterized both by its action-centeredness as well as its roots in phenomenology. The phenomenological aspects of EEC could be seen as support for trends in design emphasizing the user experience. Meanwhile, usability issues often are still approached using traditional methods based on cognitivist assumptions. In this paper, I argue for a renewed focus on improving usability from an EEC perspective. I draw mainly on a behavior-oriented interpretation of the theory, the key aspects of which are reviewed. A tentative sketch for an embodied embedded usability is proposed, doing justice to the embodied embedded nature of interaction while retaining the goal of developing technology that is easy to use in everyday practice.
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This paper addresses an approach to teaching embedded systems programming through a challenge-based competition involving robots. This pedagogical project distinguishes itself by incorporating international students from three international institutions through the Blended Intensive Program (BIP). The research findings indicate that this approach yields excellent results regarding student engagement and learning outcomes. The challenge-based program effectively promotes students' creative problem-solving abilities by combining theoretical instruction with hands-on experience in a competitive setting.
The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
