Automatic face mask detection system in public transportation in smart cities using IoT and deep learning

The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources

Redução da dor em mulheres com osteoporose submetidas a um programa de atividade física

Este estudo teve por objetivo avaliar a dor e o consumo de analgésicos em mulheres com osteoporose, após a realização de um programa de atividade física. Participaram do estudo 15 mulheres com média de idade 59±7,6 anos, com diagnóstico densitométrico em L2-L4 de osteoporose e que haviam feito uso de analgésicos para dorsalgia pelo menos três vezes por semana no mês precedente à avaliação inicial. A dor foi avaliada por questões extraídas do Osteoporosis Assessment Questionnaire, aplicadas antes e após um programa de atividade física; o escore variou de 0 (melhor, sem dor) a 10 (pior, dor diária). O programa, que consistiu em caminhadas, exercícios livres de membros superiores e inferiores e relaxamento, foi realizado duas vezes por semana durante 28 semanas consecutivas. Os dados foram tratados estatisticamente. Comparando-se as pontuações obtidas, a dor apresentou uma diminuição significativa entre a avaliação inicial (7,33±3,05) e final (4,17±2,61, p=0,0007). Observou-se também uma redução no consumo de analgésicos. Esses resultados sugerem que o programa de atividade física foi efetivo para a diminuição da dor, contribuindo para a melhora da qualidade de vida das mulheres com osteoporose.This paper aimed at evaluating the effect of a physical activity program onto the level of pain as perceived by women with osteoporosis. Fifteen women (mean age 59±7.6 years old) with bone-densitometry diagnosis of lumbar osteoporosis took part in the study; they all took analgesics at least thrice a week in the month prior to the study. Pain was assessed by questions extracted from the Osteoporosis Assessment Questionnaire both before and after the program; scores ranged from 0 (no pain) to 10 (pain everyday). The program consisted of walking, lower and upper limb free exercises, massage, and relaxation, twice a week, during 28 weeks. Data were statistically analysed. A significant decrease in pain was found after the program (from 7.33±3.05 to 4.17±2.61, p=0,0007), and a lesser use of analgesics was reported. These results suggest that the program of physical activity brought pain relief, thus contributing to improve quality of life of women with osteoporosis