Intelligent Polymer-Integrated Wearable Platforms for Sustainable IoT and Predictive Health Monitoring For Migraine Detection

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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 : 14 | 02 | Page :
    By

    Kusum Tharani,

  • Anika Ahuja,

  • Mehak Vasudeva,

  • Yug,

  • Shashi Gandhar,

  1. Professor, Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  2. Undergraduate Scholar, Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  3. Undergraduate Scholar, Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  4. Undergraduate Scholar, Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  5. Associate Professor, Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

Abstract

Migraine is a neurological disorder, and its effect on the global workforce is resultantly significant. However, the fact of the matter is the absence of notable technological breakthroughs and the fact that the technology presently available is reactive, meaning it tackles the symptoms of the attack after the attack has occurred. The requirement for this paper is, therefore, the provision of an innovative approach, and this paper will describe the intelligent and wearable approach utilizing the predictions of the attack based on the stochastic nature of the biological signals.

The proposed system monitors the essential pre-ictal physiological signal variations, which consist of the heart rate signal extracted from the photoplethysmography signal, Galvanic Skin Response, and the temperature of the skin. The essential physiological signal variations are processed through sophisticated machine learning techniques such as eXtreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) Networks, which have the precision to identify the minute trends among the patients prior to the onset of the migraine attacks. When the detection algorithm receives information about the potential onset of a migraine, it provides alert messages to the patients in the form of vibration messages and a polymer-based web interface.

Development in the hardware arena also places importance on utilizing polymers/composites to their fullest extent. This technology can be employed in the development of wearable forms that should also exhibit biocompatibility, elasticity, lightness, and strength. Its applications also include future possibilities related to energy use.

Based on the final prototype, the verification test of the prototype made it possible to establish the presence of the physiological pattern before the attack and attain an average prediction accuracy of the LSTM model of 88.9 percent. Generally, the results of the experiment underscore the huge potential for the use of AI technology in conjunction with the utilization of polymers in wearable technology for the prediction and prevention of neurology and Migraines.

Keywords: eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), pre-ictal, Galvanic Skin Response

How to cite this article:
Kusum Tharani, Anika Ahuja, Mehak Vasudeva, Yug, Shashi Gandhar. Intelligent Polymer-Integrated Wearable Platforms for Sustainable IoT and Predictive Health Monitoring For Migraine Detection. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
Kusum Tharani, Anika Ahuja, Mehak Vasudeva, Yug, Shashi Gandhar. Intelligent Polymer-Integrated Wearable Platforms for Sustainable IoT and Predictive Health Monitoring For Migraine Detection. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=240288


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Ahead of Print Subscription Original Research
Volume 14
02
Received 23/01/2026
Accepted 16/02/2026
Published 16/04/2026
Publication Time 83 Days
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