Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
Search Results
Item Impact of Stress During COVID-19 Pandemic(Institute of Electrical and Electronics Engineers Inc., 2023) Angalakuditi, H.; Bhowmik, B.The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.Item Stress Detection Using Deep Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Angalakuditi, H.; Bhowmik, B.Stress has become a prevalent issue in modern society, with various negative impacts on mental and physical health. Stress in people is a physiological and psychological reaction to an imagined threat or difficulty. Several things, including employment, relationships, income, health problems, and significant life transitions, can cause stress. Depending on the person and the circumstance, stress symptoms can vary. They frequently include emotions of worry, irritation, and restlessness, as well as physical symptoms like headaches and muscle strain. Early stress detection is crucial for effective intervention and prevention of stress-related health issues. Detecting stress in real-time can be valuable in various domains such as healthcare, mental health, human-computer interaction, and workplace performance. This paper proposes a method for detecting stress using deep learning. A set of pre-trained models are employed for stress detection. The proposed technique is evaluated with publicly available datasets. Experimented results showed that the proposed stress detection method achieves accuracy in the range of 85.71-97.50% and the loss ranging from 0.4061 to 1.8144. © 2023 IEEE.