Energy Harvesting in Implantable Bioelectronics: A Self-Sustained Approach for Long-Term Monitoring

Implantable bioelectronics are engrossed in modern healthcare where they permit continuous monitoring and treatment of chronic diseases. Unfortunately, these devices all rely on traditional battery powered systems that need replacing surgically, and this is expensive and uncomfortable for the patient. To solve this problem, this study proposes a self-sustained energy management approach based on energy harvesting with an AI driven CNN application with MLP as the architecture. Piezoelectric and thermoelectric generators exploit the body heat and motion to harvest energy and store the converted energy in capacitors and supercapacitors for continuous operation. The real time sensor data is fed into the CNN component, which extracts relevant features and identifies energy consumption patterns; FNN identifies the future energy requirements and allocates energy optimally. A method is created to train and validate the model using an IEEE DataPort dataset including sensor readings, power usage metrics, and energy harvesting patterns. Results from the evaluation show that the CNN FNN prediction model achieves very good prediction accuracy, improves energy harvest efficiency and power consumption up by 20-30% for prolonging device lifespan. It is then compared with traditional battery powered and passive storage methods as the latter is found to be comparatively inferior to this AI based approach with regards to reduced maintenance and increased reliability. This work makes way for the next generation of autonomous implantable bioelectronics that operate well without frequent external interventions through allowing self-sustaining functionality. Real world hardware implementation, use of alternative energy harvesting sources, as well as refinement of the AI models towards higher accuracy and adaptability in dynamic patient conditions will be future subjects for work.