About this Research Topic
It is known that individuals who are well engaged during learning new skills or performing rehabilitation training tend to have better achievements than their peers do. Measuring those individuals’ attention and engagement levels during the training and proposing methods to continuously stimulate their attention is, therefore, one of the main keys to successful training. By numerically scaling the level of individual's attention during the training sessions and illustrating it in a simplified manner, instructors can adapt their training methodologies, speed, and attitude to ensure the individuals’ engagement is at the maximum level. Recent developments in computer vision and machine learning can provide incredible tools to detect the attention level of individuals during training or rehabilitation. Furthermore, machine learning and the concept of Artificial Intelligence (AI) may also assist the instructors in understanding the individuals' interest and crafting personal exercises that suit their interests and abilities.
This Research Topic will cover research theories and applications in attention detection systems of the individuals during skill acquisition training or rehabilitation. The topic will also include the methodologies to represent an individual’s attention data in an easy and informative way to the instructors in real time. Such representation can assist the instructors in making the right decisions in several areas related to the interest of the individuals. In the education field, for instance, it could be classifying the students in classes according to their personal tendencies, or adjusting the teaching style to suit the students' interests.
• Applications for individuals’ attention systems
• Applications for real-time attention feedback to the instructor.
• Use of different intelligent modalities for attention tracking such as robots and mobile apps.
• User studies of attention tracking systems
• Ethical issues of attention based systems and their integration in different platforms.
Keywords: Attention Assessment, Classroom, Computer Vision, Deep Learning, Real Time Feedback
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