Signal Drift Compensation in Polymer-Based Wearable Biosensors Using Data Processing Techniques

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

    G. Aloy Anuja Mary,

  • S A Yuvaraj,

  • S Farook,

  • V Priyanka,

  1. Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R& D Institute of Science and Technology, Chennai, Tamil Nadu, India
  2. Professor, Department of Biomedical Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamil Nadu, India
  3. Professor, Department of Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, India
  4. Assistant Professor, Department of Electronics and Communication Engineering, KCG College of Technology, Karapakkam, Chennai, Tamil Nadu, India

Abstract

Polymer-based wearable biosensors have emerged as promising platforms for continuous physiological monitoring due to their mechanical flexibility, low operating voltage, and compatibility with soft biological interfaces. However, their long-term deployment remains challenging because of signal drift caused by polymer ageing, hydration–dehydration cycles, ionic trapping, and environmental variations. These effects introduce baseline fluctuations and sensitivity degradation, which compromise the reliability and interpretability of physiological measurements. This study proposes a data-processing–driven framework for compensating signal drift in polymer-based wearable biosensors using robust time-series analysis techniques. The approach models the measured biosensor signal as a combination of physiological dynamics, polymer-ageing drift, motion artefacts, and measurement noise, enabling explicit separation of slow drift components from genuine physiological variations. A hybrid compensation mechanism integrating robust trend estimation and adaptive state-space tracking is developed to estimate baseline and gain drift while preserving clinically relevant signal dynamics. The framework is evaluated using a publicly available wearable health monitoring dataset containing multivariate physiological signals such as heart rate, temperature, oxygen saturation, blood pressure, and activity information. Dataset-level analysis demonstrates wide physiological variability and strong activity-dependent behaviour, highlighting the necessity of context-aware drift correction strategies. Experimental evaluation shows that the proposed processing pipeline effectively stabilizes long-term wearable signals without suppressing genuine physiological transitions. By combining drift modeling, adaptive correction, and reliability scoring, the proposed framework establishes a reproducible foundation for improving the stability and usability of polymer-based wearable biosensors in continuous health monitoring applications.

Keywords: Polymer wearable biosensors; Signal drift; Time-series processing; Long-term reliability; Activity-aware monitoring.

How to cite this article:
G. Aloy Anuja Mary, S A Yuvaraj, S Farook, V Priyanka. Signal Drift Compensation in Polymer-Based Wearable Biosensors Using Data Processing Techniques. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
G. Aloy Anuja Mary, S A Yuvaraj, S Farook, V Priyanka. Signal Drift Compensation in Polymer-Based Wearable Biosensors Using Data Processing Techniques. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=243027


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Ahead of Print Subscription Original Research
Volume 14
03
Received 28/02/2026
Accepted 16/03/2026
Published 06/05/2026
Publication Time 67 Days
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