Real time stress monitoring system for workers using IOT and Machine Learning

Abstract The work presents the design of a real-time stress estimation system using wearable sensors and machine learning techniques. Through ongoing sensing of physiological responses such as heart rate, skin conductance, and motion data, the system is able to estimate levels of stress in close to real time. A slim machine learning approach offers fast and effective processing in order for the system to be used in real time. Customers receive immediate alerts and stress-reducing advice specifically for them through a smartphone application. The technology also solves main pain points like noise in signals, delay, and data privacy. Its flexibility and accuracy allow it to be applied to many areas including health care, office wellness, and personal mental well-being assistance Key Words: Real-time stress prediction, worker stress monitoring, physiological sensors, machine learning, vibration therapy, workplace wellness, stress relief system.