Design and Optimization of Wearable Sensor Technologies for Real-Time Biomedical Monitoring

Wearable sensor technologies have significantly advanced real-time biomedical monitoring by enabling continuous, non-invasive tracking of vital physiological parameters. Despite their potential, current wearable sensors face limitations in accuracy, power efficiency, data security, and user comfort, restricting their widespread adoption in healthcare applications. This research focuses on the design and optimization of wearable biomedical sensors, integrating multi-modal sensor fusion, AI-driven data processing, self-powered energy solutions, and enhanced security frameworks to improve performance and usability. The study analyzes sensor accuracy across different modalities, highlighting the impact of motion artifacts and environmental interference on measurement reliability. Additionally, it explores low-power electronics and energy-harvesting mechanisms, such as piezoelectric and thermoelectric generators, to extend battery life and enable continuous monitoring. AI-based predictive analytics and blockchain-secured health data management are proposed to enhance real-time health insights and data protection. The research also emphasizes material innovation, developing flexible, stretchable, and biocompatible sensor materials for improved wearability. By evaluating experimental data from existing wearable technologies, this study provides quantifiable insights into sensor performance, power consumption, and cybersecurity risks, paving the way for the next generation of smart, energy-efficient, and clinically validated wearable health monitoring devices. These findings contribute to bridging the gap between consumer wearables and medical-grade diagnostics, ensuring their integration into personalized healthcare, telemedicine, and remote patient monitoring systems.