• C . SANJAY
• S . VISHNUPRIYAN
• P . YOGESH
• VIJAYAKUMAR D

The integration of IoT in healthcare has paved the way for real-time patient monitoring, offering both predictive and diagnostic advantages. This project proposes a health monitoring system to detect stress and forecast vital signs using wearable sensors and machine learning models. By capturing physiological data and processing it through cloud-based analytics, the system empowers proactive healthcare management. Beyond traditional health assessments, this model focuses on non-invasive data collection and intelligent interpretation. Stress classification is handled using machine learning classifiers, while time-series models like LSTM forecast health anomalies. This two-pronged approach makes the system suitable for early detection and remote patient care, especially in areas with limited medical access.

IoT, Stress Detection, Wearable Sensors, Machine Learning, LSTM, Vital Signs Forecasting, Biomedical Signals, Real-Time Monitoring.

"Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.i345-i346, May-2025, Available :http://www.jetir.org/papers/JETIR2505936.pdf

"Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppi345-i346, May-2025, Available at : http://www.jetir.org/papers/JETIR2505936.pdf