RESUMO
Human emotions can be seen as a valuable variable to explore in Human-Computer Interaction for effective, efficient, and satisfying interface development. The inclusion of appropriate emotional triggers in the design of interactive systems can play a decisive role in users' acceptance or rejection. It is well known that the major problem in motor rehabilitation is the high dropout rate resulting from the frustrated expectations given the typical slow recovery process and consequent lack of motivation to endure. This work proposes grouping a collaborative robot with one specific augmented reality equipment to create a rehabilitation system where some gamification levels might be added to provide a better and more motivating experience to patients. Such a system, as a whole, is customizable to adapt to each patient's needs on the rehabilitation exercises. By transforming a tedious exercise into a game, we expect to create an additional layer of enjoyment that can help in triggering positive emotions and stimulate users to continue the rehabilitation process. A pre-prototype was developed to validate this system's usability, and a cross-sectional study using a non-probabilistic sample of 31 individuals is presented and discussed. This study included the application of three standard questionnaires on usability and user experience. The analyses of these questionnaires show that the majority of the users found the system easy and enjoyable. The system was also analysed by a rehabilitation expert who gave a positive output regarding its usefulness, and positive impact on its use in the upper-limb rehabilitation processes. These results clearly encourage further development of the proposed system.
RESUMO
Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user's situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.