Paper Key : IRJ************210
Author: Pranav Hariharrao Kulkarni,Dr.r.v.babar,Aamrapali Sarkate,Kalpesh Pawar,Prafullata Ohol
Date Published: 11 Nov 2024
Abstract
In the fast-paced and demanding world of the technology often encounter high levels of stress due to heavy workloads, tight deadlines, and dynamic work environments. Prolonged exposure to such stress can negatively impact both personal well-being and professional productivity. Traditional methods of stress detection, such as self-reported questionnaires and physiological assessments, are often intrusive and lack real-time applicability. As a result, there is a growing need for more effective and less invasive stress monitoring solutions. This research aims to address this challenge by developing a novel stress detection system tailored specifically for peoples. Leveraging advances in image processing and machine learning, the system analyze facial expressions and visual cues that are indicative of stress. By employing sophisticated algorithms, the system can accurately classify and interpret stress levels in real-time. Furthermore, the system integrates continuous feedback mechanisms by regularly collecting employee surveys to track stress levels over time and evaluate the effectiveness of interventions. With its real-time capabilities and non-intrusive design, this system offers a significant improvement over conventional stress detection methods, providing employees and organizations with valuable insights to foster healthier work environments