Highlights A survey is conducted of different age groups from various educational institutes in Delhi - National Capital Region (NCR), India. The impact of COVID -19 on the students of different age groups: time spent on online classes and self-study, medium used for learning, sleeping habits, daily fitness routine, and the subsequent effects on weight, social life, and mental health. It is observed that in order to deal with stress and anxiety, participants adopted different coping mechanisms and also sought help from their near ones. It is also reported that the student’s engagement on social media platforms among different age categories. This study suggests that public authorities should take all the necessary measures to enhance the learning experience by mitigating the negative impacts caused due to the COVID -19 outbreak. It is investigated and analysed the potential consequences of the COVID-19 pandemic on the life of students. Our research shows that there is a wide gap between the government's policy aspirations and the implementation of these online education policies at the grassroots level. Moreover, our study attempts to assess the mental situation of students of different age groups using different parameters including sleeping habits, daily fitness routine, and social support. Further, we analyse different coping mechanisms used by students to deal with the current situation.

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Falls are one the leading causes of accidental death for all people, but the elderly are at particularly high risk. Falls are severe issue in the care of those elderly people who live alone and have limited access to health aides and skilled nursing care. Conventional vision-based systems for fall detection are prone to failure in conditions with low illumination. Therefore, an automated system that detects falls in low-light conditions has become an urgent need for protecting vulnerable people. This paper proposes a novel vision-based fall detection system that uses object tracking and image enhancement techniques. The proposed approach is divided into two parts. First, the captured frames are optimized using a dual illumination estimation algorithm. Next, a deep-learning-based tracking framework that includes detection by YOLOv7 and tracking by the Deep SORT algorithm is proposed to perform fall detection. On the Le2i fall and UR fall detection (URFD) datasets, we evaluate the proposed method and demonstrate the effectiveness of fall detection in dark night environments with obstacles.

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The potential of automated severity assessment of COVID-19 pneumonia is immense due to its ability to facilitate clinical decision-making. It enables efficient escalation or de-escalation of COVID-19 care. In this work, we propose an efficient pipeline based on weakly-supervised learning for severity score prediction. In the first stage, Attention feature fusion (AFF)-ResNet-101 is trained on five large Chest X-ray (CXR) datasets. In the second stage, using transfer learning on 94 posteroanterior (PA) COVID-19 CXR images, refined feature maps are extracted. These feature descriptors are then mapped to severity ratings based on Geographic Extent score and lung opacity output using the linear regression model. Experimental results show the efficacy of our proposed architecture for the severity score prediction.