Using ML algorithm and HCI to analyse stress-related physiological health parameters of IT sector employees using wearable smart watches

Abstract

Stress is a part of everyday life, but when it becomes chronic, it can seriously affect our health—impacting everything from our heart and immune system to hormone balance. This research looks into new ways of understanding and managing stress by using a combination of machine learning and user-friendly technology. By digging into past studies and medical data, the goal is to better identify the signs of stress and find smarter ways to track it.

Machine learning plays a big role here by helping make sense of complex data collected from people’s bodies—like heart rate or skin responses. These algorithms can spot patterns, predict stress levels, and even adapt over time to better support users. At the same time, user-friendly interfaces—think mobile apps or wearable devices—make it easier for people to monitor their stress and get helpful tips in real time. This teamwork between technology and human-centered design makes stress management more practical and accessible.

The system relies on modern wearables that collect data continuously and connect with health apps or records. It also takes into account important concerns like privacy and informed consent, ensuring users feel safe and in control. In the bigger picture, this approach could lighten the load on healthcare systems and help people manage stress before it becomes a serious problem. Looking ahead, the researchers suggest refining the technology, involving users more in the design process, and testing these tools in real-life settings to make them even more effective.