Published Paper Details:

Continuous monitoring of vital parameters is essen- tial in modern healthcare, particularly for patients with chronic illnesses and elderly populations. Conventional wearable devices typically operate at fixed sampling rates, resulting in unnecessary energy consumption and reduced device lifetime. This paper presents a Python-based simulation framework for dynamic mode switching in health monitoring embedded systems. The proposed approach employs a Finite State Machine (FSM) to adaptively transition between Normal, Alert, and Critical states based on variations in heart rate (HR) and blood oxygen saturation (SpO2). The framework was implemented using open- source Python libraries (NumPy, SciPy, and Matplotlib) for signal generation, threshold detection, and real-time visualization. Simulation results demonstrate that the system conserves energy during stable conditions while ensuring responsiveness during abnormal events. Unlike prior studies that rely heavily on MATLAB/Simulink, this work highlights Python as a cost- effective and flexible platform for rapid prototyping of embedded healthcare solutions, with strong scalability toward IoT integra- tion and predictive analytics.