Manish K. Assudani,
Jadhav Nitin B.,
Vimal Bibhu,
Ramnath V.,
D. Sandhya Rani,
G. Vijaya Kumar,
- Associate Professor, Department of Computer Science and Engineering, Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India
- Associate Professor, Department of Science & Technology, Krishna Institute of Science and Technology, Krishna Vishwa Vidyapeeth “Deemed to be University”, Karad, Satara, Maharashtra, India
- Associate Professor, Department of Computer Science & Engineering, Noida International University, Greater Noida, Uttar Pradesh, India
- Associate Professor, Department of Allied Health, Meenakshi College of Allied Health Sciences, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research, Kanchipuram, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India
- Associate Professor, Department of Computer Science and Engineering, Pragati Engineering College, Kakinada, Andhra Pradesh, India
Abstract
The fast development of healthcare technologies has generated the increased need of real-time, correct, and effective medical monitoring systems. Polymer composite biosensors have come out as a viable solution since they are flexible, biocompatible and highly sensitive in detecting physiological and biochemical signals. Nevertheless, traditional cloud-based data processing systems bring about latency, bandwidth issues, and privacy risks, and thus are not suitable to use in time-sensitive medical services. To overcome these issues, this paper develops an edge-computing-based framework on polymer composite biosensors to facilitate real-time medical analytics. The suggested system incorporates the use of polymer nanocomposites to create biosensors and edge computing devices which will be used to carry out local data processing, signal conditioning and smart decision-making. It is a system architecture created in detail through the inclusion of biosensing/edge intelligence, secure communication, and application layers. Mathematical models are developed to depict the biosensor response, signal processing, optimization of latency, and predictive analytics of AI. Experimental analysis proves that the suggested system can be highly accurate (up to 98%), the latency can be significantly decreased (by 4060%), and the system may be more energy-efficient than the traditional cloud-based and IoT-based ones. The bar and radar charts are used to conduct a comparative analysis of the proposed framework with other frameworks, which reveals that the former is more responsive, has better privacy, and is more scalable. The findings affirm that combining the edge computing (EC) with polymer composite biosensors is an effective and solid solution to the smart healthcare system of the next generation.
Keywords: Polymer composite biosensors, edge computing, real-time medical analytics, artificial intelligence, machine learning, IoT healthcare, wearable devices, signal processing, smart healthcare systems, predictive analytics.
[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]
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Journal of Polymer & Composites
| Volume | 14 | |
| Special Issue | 04 | |
| Received | 01/05/2026 | |
| Accepted | 24/06/2026 | |
| Published | 21/08/2026 | |
| Publication Time | 112 Days |