Advancements in technology have increased demand for systems that continuously monitor the cardiovascular system in a non-invasive, energy-efficient way. Wearable sensors, in their current form, have many drawbacks: they require external power sources and are made of rigid components. This can impact the user experience, the system, the sensor's ability to perform real-time health assessments, and the ability to perform multiple evaluations over time. This paper investigates the use of a soft, adjustable sensing system based on triboelectric nanogenerator (TENG) technology for the assessment of cardiovascular signals and predictive analysis of anomalies. Proposed systems use synthesized biomechanical energy from user movements to power themselves, eliminating the need for external power sources. Sophisticated signal analysis and processing are utilized to measure and monitor cardiovascular parameters, which in this case are derived from triboelectric signals and measure/monitor heart rate variability, waveforms, and the rate/characteristics of blood flow. Additionally, a machine learning predictive model is incorporated to analyze and monitor patterns and assess for anomalies in the user's cardiovascular system, identifying those most at risk for cardiac disease. Simulations and experiments indicate that the proposed system outperforms existing systems in predictive signal analysis and monitoring. Based on the evidence, the proposed system allows for a 35% reduction in external power supply and a 22% increase in predictive analysis of system alarms. Over time, the proposed system can be deployed in a real-world setting. The system's flexible design allows the user to capture signals from their physiological systems without discomfort. By integrating self-powered sensing with intelligent analytics and soft electronics, this research offers a novel and adaptable solution for next-generation wearable health care systems. The proposed framework contributes to innovative interdisciplinary research in wearable technology and applied biomedical engineering, with particular emphasis on remote patient monitoring, preventive health care, and innovative medical systems.
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