Adaptive Drift Correction in Polymer-Based Wearable Biosensors via Data-Driven Signal Modeling

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    A Sabari Vani,

  • Sheeba Santhosh,

  • Geetha Prahalad,

  • M Ram Prasad Reddy,

  1. Assistant Professor, Department of Biomedical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
  2. Associate Professor, Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
  3. Professor, Department of Electronics and Communication Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh, India
  4. Professor, Department of Electrical and Electronics Engineering, Aditya College of Engineering, Madanapalle, Andhra Pradesh, India

Abstract

Polymer-based wearable biosensors have emerged as a promising technology for continuous health monitoring due to their mechanical flexibility, biocompatibility, and suitability for long-term physiological interfacing. However, prolonged exposure to biofluids, environmental variability, and mechanical deformation introduces signal drift, which significantly degrades measurement accuracy and limits clinical reliability. This paper presents a data-driven methodology for compensating signal drift in polymer-based wearable biosensors using adaptive signal processing and machine learning techniques. The proposed framework operates entirely at the software level, enabling continuous drift correction without requiring periodic physical recalibration. Wearable health monitoring datasets are employed to model realistic non-stationary signal behavior, and drift is treated as a structured, learnable component rather than random noise. Experimental evaluation demonstrates substantial reduction in baseline shift and drift rate, along with significant improvements in root mean square error and signal-to-noise ratio. The results confirm that the proposed approach enhances long-term signal stability while preserving physiological information, making it suitable for real-time wearable health monitoring applications.

Keywords: Polymer-based wearable biosensors; Signal drift compensation; Wearable health monitoring; Machine learning; Biomedical signal processing.

How to cite this article:
A Sabari Vani, Sheeba Santhosh, Geetha Prahalad, M Ram Prasad Reddy. Adaptive Drift Correction in Polymer-Based Wearable Biosensors via Data-Driven Signal Modeling. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
A Sabari Vani, Sheeba Santhosh, Geetha Prahalad, M Ram Prasad Reddy. Adaptive Drift Correction in Polymer-Based Wearable Biosensors via Data-Driven Signal Modeling. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=243053


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Ahead of Print Subscription Original Research
Volume 14
03
Received 28/02/2026
Accepted 13/03/2026
Published 06/05/2026
Publication Time 67 Days
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