Photoplethysmography-based Stress Detection using Machine Learning

Stress-related health issues are growing day by day. Therefore, it is important to find easier ways for accurate stress tracking on a daily basis. This research is an attempt to use photoplethysmography sensors to track stress levels in individuals. Photoplethysmography is a non-invasive optical technique used to detect blood volume changes in the microvascular bed of tissue. PPG sensors use various features like heart rate variability and pulse amplitude The main aim is to achieve real-time stress detection on a day-to-day basis. The system utilizes various machine learning algorithms to analyze signals and track stress patterns in people for stress management. The system also includes the use of different classifiers including Random Forest (RF), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM). The paper focuses on providing an effective way to track and manage stress without interfering in the individual's dayto- day activities.