Sitting posture recognition systems have gained significant attention due to their potential applications in various domains, including healthcare, ergonomics, and human-computer interaction. This paper presents a comprehensive literature review and analysis of existing sitting posture recognition systems. Through an extensive examination of relevant research articles and conference papers, we identify and analyze the underlying technologies, methodologies, datasets, performance metrics, and applications associated with these systems. The review encompasses both traditional methods, such as vision-based approaches and sensor-based techniques, as well as emerging technologies such as machine learning and deep learning algorithms. Additionally, we examine the challenges, constraints, and future trends in the field of sitting posture recognition systems. Researchers, practitioners, and policymakers who want to comprehend the most recent developments and latest trends in sitting posture recognition technology will find great value in this study.

Students in the twenty-first century are accustomed to using technology in all aspects of their lives and have never known a world without it; the classroom is no exception. Augmented reality (AR) is a technology that bridges the virtual and physical worlds to make learning more engaging and enjoyable. In this paper, we present a mobile application aimed at novice learners that makes use of technology for the teaching and learning of computer system engineering concepts. Currently, students typically learn about finite-state machine (FSM) concepts from lectures, tutorials, and practical hands-on experience combined with commercial timing simulation tools. We aimed to enhance these traditional, lecture-based instruction and information delivery methods. We developed an AR-based FSM visualization tool called AR4FSM to help students more easily grasp concepts through immersion and natural interaction with an FSM. We used a blend of multimedia information, such as text, images, sound, and animations superimposed on real-world-state machine diagrams, presenting the information in an interactive and compelling way. An experiment with 60 students showed that the app was perceived positively by the students and helped to deliver FSM-related concepts in a way that was easier to understand than traditional, lecture-based teaching methods. This instruction methodology not only engaged the students but also motivated them to learn the material. The findings of this study have inspired us to use this application to teach FSM topics in the classroom.