In the modern world characterized by
technological advancements, stress has become an
increasingly prevalent issue affecting individuals across
various walks of life. Despite material prosperity, the
pressures associated with contemporary living often led
to dissatisfaction and stress, which can manifest as
mental, emotional, and physical strain. Effective stress
management systems are crucial for assessing and
addressing these stress levels, given their potential to
disrupt socioeconomic well-being. According to the
World Health Organization (WHO), a quarter of the
global population grapples with stress-related mental
health concerns. The consequences of stress encompass
not only personal well-being but also extend to
socioeconomic challenges, including reduced work
concentration, strained interpersonal relationships,
feelings of hopelessness, and, in extreme cases, even
suicide. Consequently, counseling services are essential
for aiding individuals in coping with stress. While it is
virtually impossible to eliminate stress entirely, proactive
measures can play a pivotal role in its management.
Accurate assessment of stress requires the expertise of
medical and physiological professionals. A well-
established method for stress identification involves the
use of questionnaires. This research project's primary
objective is to employ advanced machine learning and
image processing techniques to detect signs of stress in
IT professionals. Unlike previous stress detection
technologies, our approach takes into account employees'
emotional states and facilitates real-time detection. The
system combines both periodic and instantaneous
emotion recognition, contributing to the minimization of
health risks associated with stress and enhancing the
overall well-being of IT employees and the organizations
they work for.By leveraging the insights into IT employees'
emotions, businesses can offer targeted support and
achieve improved outcomes.This accuracy
underscores the effectiveness of our system in identifying
stress indicators among IT professionals. Through our
technology, we strive to create a more refined stress
detection approach that goes beyond conventional
methods and provides real-time insights into employees'
emotional states, thus enabling timely interventions.Ultimately, our system's implementation holds the
potential to enhance the overall work environment,
foster employee well-being, and contribute to the success
of IT companies.
Keywords : Stress detection, IT Employees, Machine Learning, Image Processing, Convolutional Neural Networks, Employee well-being, Productivity, Mental health, Stress management, Real-time monitoring.