StressSense: An IoT-Enabled Platform for Stress Level Prediction, Prevention, and Methods There of

As eloquently articulated by the esteemed sociologist and writer Chelsea Erieau, stress is a dynamic force, akin to an accelerator that exerts its influence, invariably driving individuals either forward or backward. This proposed project emerges as a beacon of hope, offering a sophisticated and highly effective solution for the detection and management of stress levels. It harnesses the power of cutting-edge physiological sensors, encompassing a pulse sensor (MAX30102), a Galvanic Skin Response sensor (GSR Grove v2.0), a body temperature sensor (DS18B20), and an array of other essential data sources like the smart watches (Apple Watch Ultra and Noise BT) for vitals. In conjunction with this, it introduces a user-centric application interface that has been meticulously crafted for the real-time analysis of sensor data, representing a revolutionary step forward in stress management. This interface leverages the prowess of advanced machine learning (ML) libraries and techniques, including TensorFlow and PyTorch for deep learning, regression models, and Scikit-learn for ensemble learning, as well as kNN models. It provides a dynamic and robust platform to monitor physiological responses and offer personalized recommendations. This user-centric approach empowers individuals to navigate the challenges of stress and achieve well-being. This innovative solution marks a significant shift toward personalized stress management, offering a lifeline to individuals seeking to mitigate the negative impacts of stress and lead a healthier, balanced life.