ABSTRACT
We developed a hands-on activity using the game Jenga to demonstrate the links between health behaviors, chronic and infectious diseases, and community well-being and resilience. For the activity, K-12 students worked together in small teams (4-8 students) and were given two Jenga towers (tower A and tower B), each representing a community of individuals. The goal was to keep both towers standing. Teams were presented with strips of paper labeled with either a "health behavior" (e.g., nutrition, body weight maintenance, physical activity) or a "disease" (e.g., heart disease, diabetes, COVID-19) and instructions on whether to add or remove blocks from each tower. When presented with a health behavior, students added blocks to tower A for positive health behaviors (e.g., not smoking) and removed blocks from tower B for negative health behaviors (e.g., smoking). When a disease was presented students removed blocks from both towers, but fewer blocks were removed from tower A compared with tower B, demonstrating lower disease rates or severity in that community. As the activity progressed, tower A retained more blocks than tower B. For the finale, students observed that the greater strength and stability of tower A allowed it to withstand a simulated natural disaster such as an earthquake better than tower B. This activity was delivered to 15 science classes and 225 students ranging from 6th to 12th grade. Students were able to describe the connections between positive health behaviors and lower rates of disease and how, taken together, these impact community health, well-being, and resilience.NEW & NOTEWORTHY We describe how K-12 students played Jenga to learn about the connections between health living habits, disease, and community well-being and resilience.
Subject(s)
COVID-19 , Humans , Students , ExerciseABSTRACT
Nowadays, identity theft is an alarming issue with the growth of e-commerce and online services. Moreover, due to the Covid-19 pandemic, society has been pushed towards the usage of masks for people to safely interact with one another. It is hard to recognize a person if the face is mostly covered, even more so to artificial intelligence who have more difficulty identifying a masked individual. To further protect personal information and to develop a secure information system, more comprehensive bio-metric approaches are required. The currently used facial recognition systems are using biometrics such as periocular regions, iris, face, skin tone and racial information etc. In this paper, we apply a deep learning-based authentication approach using periocular biometric information to enhance the performance of the facial recognition system. We used the Real-World Masked Face Dataset (RMFD) and other datasets to develop our system. We implemented some experiments using CNN model on the periocular region information of the images. Hence, we developed a system that can recognize a person from only using a small region of face, which in this case is the periocular information including both eyes and eyebrows region. There is only a focus on the periocular region with our model in the view of the fact that the periocular region of the face is the main reliable source of information we can get while a person is wearing a face mask. © 2022, Springer Nature Switzerland AG.