ABSTRACT
In this study, we present a visual servo control framework for fully automated nasopharyngeal swab robots. The proposed framework incorporates a deep learning-based nostril detection with a cascade approach to reliably identify the nostrils with high accuracy in real time. In addition, a partitioned visual servoing scheme that combines image-based visual servoing with axial control is formulated for accurately positioning the sampling swabs at the nostril with a multi-DOF robot arm. As the visual servoing is designed to minimize an error between the detected nostril and the swab, it can compensate for potential errors in real operation, such as positioning error by inaccurate camera-robot calibration and kinematic error by unavoidable swab deflection. The performance of the visual servo control was tested on a head phantom model for 30 unused swabs, and then compared with a method referring to only the 3D nostril target for control. Consequently, the swabs reached the nostril target with less than an average error of 1.2±0.5 mm and a maximum error of 2.0 mm via the visual servo control, while the operation without visual feedback yielded an average error of 10.6±2.3 mm and a maximum error of 16.2 mm. The partitioned visual servoing allows the swab to rapidly converge to the nostril target within 1.0 s without control instability. Finally, the swab placement at the nostril among the entire procedure of fully automated NP swab was successfully demonstrated on a human subject via the visual servo control. © 2022 IEEE.
ABSTRACT
The aims of nursing training include not only mastering skills but also fostering the competence to make decisions for problem solving. In prenatal education, cultivating nurses' knowledge and competence of vaccine administration is a crucial issue for protecting pregnant women and newborns from infection. Therefore, obstetric vaccination knowledge has become a basic and essential training program for nursing students. However, most of these training programs are given via the lecture-based teaching approach with skills practice, providing students with few opportunities to think deeply about the relevant issues owing to the lack of interaction and context. This could have a negative impact on their learning effectiveness and clinical judgment. To address this problem, a mobile chatbot-based learning approach is proposed in this study to enable students to learn and think deeply in the contexts of handling obstetric vaccine cases via interacting with the chatbot. In order to verify the effectiveness of the proposed approach, an experiment was implemented. Two classes of 36 students from a university in northern Taiwan were recruited as participants. One class was the experimental group learning with the proposed approach, while the other class was the control group learning with the conventional approach (ie, giving lectures to explain the instructional content and training cases). The results indicate that applying a mobile chatbot for learning can enhance nursing students' learning achievement and self-efficacy. In addition, based on the analysis of the interview results, students generally believed that learning through the mobile chatbot was able to promote their self-efficacy as well as their learning engagement and performance. Practitioner notes What is already known about this topic Issues relevant to AI technology in education have been extensively discussed and explored around the world. Among the various AI systems, the potential of chatbots has been highlighted by researchers owing to the user-friendly interface developed using the natural language processing (NLP) technology. Few studies using AI chatbots in professional training have been conducted. What this paper adds In this study, a mobile chatbot was used in a nursing training program to enhance students' learning achievement and self-efficacy for handling vaccine cases. The mobile chatbot significantly improved the students' learning achievement and self-efficacy in comparison with the conventional learning approach in the vaccine training program. From the interview results, it was found that the students generally believed that the mobile chatbot was able to promote their self-efficacy as well as learning engagement and performances in the vaccine training program. Implications for practice and/or policy Mobile chatbots have great potential for professional training owing to their convenient and user-friendly features. It would be worth applying mobile chatbots as well as other NLP-based applications to other professional training programs in the future. © 2021 British Educational Research Association.
ABSTRACT
Trends are important to us as researchers and practitioners in higher education (Boer et al., 2002). Trends influence decisions about funding for research, decisions about organisational infrastructure, and the establishment of new degrees. The dictionary definition of trend is a general direction in which something is developing or changing, and also a fashion. According to these definitions, a trend is both measurable with evidence that confirms the trend’s existence and direction, and at the same time emergent, and sometimes fleeting. There is little in the design of tertiary institutions that encourages a timely response to trends, and yet there are people within universities who invest significant time in providing support for the implementation of new types of technology or pedagogical approaches for teaching and research staff. Outside of university settings, these would be R&D teams – testing the use of the latest technology and figuring out how it could be implemented in new contexts. White papers are often the way that these findings are shared in other areas. In education technology in higher education, we share innovation in practice through conferences and journals as well as online social networks and communities of practice such as Twitter and professional society networking platforms. The sharing of research in the area of education technology is subject to a significant lag for several reasons. Social networks reach some, but not all of the community, and journal articles can sometimes take months to move from submission to publication. Since the outbreak of the COVID-19 pandemic, sharing at conferences has been challenging, but even outside of the current restrictions on travel, conference attendance was reliant on funding from organisations or by individuals. Despite this, research in this field still experiences trends in emerging technology, methodological and theoretical approaches, and pedagogical practice © Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant AJET right of first publication under CC BY-NC-ND 4.0.