GANs for Enhancing Wearable Biosensor Data Accuracy

Abstract

Wearable biosensors have become indispensable in the realm of health monitoring, providing real-time data on physiological parameters such as heart rate, temperature, and glucose levels. Despite their increasing adoption, these devices often face challenges related to data accuracy, mainly due to sensor noise, signal artifacts, and inconsistencies in sensor quality. Such inaccuracies pose a significant barrier to the reliable use of biosensors in healthcare, reducing their effectiveness for both clinical applications and personal health tracking. To address these limitations, the implementation of Generative Adversarial Networks (GANs) offers a novel and promising solution.

GANs consist of two neural networks—the generator and the discriminator—that operate in an adversarial manner. The generator creates synthetic data samples, while the discriminator attempts to distinguish between real and generated data, leading to the continuous refinement of data quality. In the context of wearable biosensors, GANs hold immense potential to improve data accuracy by filtering out noise, correcting signal distortions, and producing high-fidelity synthetic data that mimic real biosensor outputs.