Autonomous Calibration of Medical Devices Using Synthetic Biosignals and Adaptive Learning

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | 02 | Page :
    By

    Ms. Amita Madhukar Tembhare,

  • Asmita S. Gawande,

  1. Research Scholar, Department of Electronics, Brijlal Biyani Science College, Amravati, Maharashtra, India
  2. Research Scholar, Department of Electronics, Brijlal Biyani Science College, Amravati, Maharashtra, India

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.

Keywords: AI-driven calibration, Biosignal simulation, GAN, VAE, Reinforcement learning, Medical devices, IoT healthcare, Edge AI, ECG, EMG, Adaptive systems

How to cite this article:
Ms. Amita Madhukar Tembhare, Asmita S. Gawande. Autonomous Calibration of Medical Devices Using Synthetic Biosignals and Adaptive Learning. Journal of Instrumentation Technology & Innovations. 2026; 16(02):-.
How to cite this URL:
Ms. Amita Madhukar Tembhare, Asmita S. Gawande. Autonomous Calibration of Medical Devices Using Synthetic Biosignals and Adaptive Learning. Journal of Instrumentation Technology & Innovations. 2026; 16(02):-. Available from: https://journals.stmjournals.com/joiti/article=2026/view=245765


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Ahead of Print Subscription Review Article
Volume 16
02
Received 08/05/2026
Accepted 26/05/2026
Published 03/06/2026
Publication Time 26 Days
13

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