Autonomous Calibration of Medical Devices Using Synthetic Biosignals and Adaptive Learning

Abstract

The accuracy and reliability of modern biomedical diagnostic devices are critically dependent on effective calibration mechanisms capable of handling dynamic physiological and environmental variations. Conventional calibration approaches, which rely on static reference signals and manual adjustments, are inadequate in addressing challenges such as sensor drift, noise interference, motion artifacts, and long-term performance degradation. To overcome these limitations, this research proposes an innovative AI-driven adaptive biosignal simulation and calibration architecture for next-generation medical devices. The proposed framework integrates advanced deep learning models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to generate high-fidelity, multi-modal synthetic biosignals such as ECG, EMG, and EEG. These signals replicate diverse physiological conditions, pathological patterns, and real-world noise scenarios, enabling comprehensive testing and validation of diagnostic systems. Furthermore, a reinforcement learning-based adaptive calibration engine is developed to continuously monitor device performance, detect drift and inaccuracies, and autonomously adjust system parameters such as gain, offset, and filtering coefficients in real time. To ensure scalability and real-world applicability, the architecture incorporates edge-AI capabilities, enabling on-device self-calibration for wearable and IoT-based healthcare systems. The framework is designed in compliance with international medical standards to ensure safety, reliability, and clinical acceptance. Experimental validation demonstrates improved calibration accuracy, enhanced robustness under noisy conditions, and reduced dependency on manual intervention. This research contributes a novel, intelligent, and self-correcting calibration paradigm that bridges the gap between biosignal simulation and adaptive device optimization. The proposed system has significant potential to enhance the performance, reliability, and longevity of biomedical diagnostic devices, thereby supporting more accurate clinical decision-making in modern healthcare environments.