Generative AI-Driven Hyper Personalized Wearable Healthcare Devices: A New Paradigm for Adaptive Health Monitoring

Document Type : Research Paper

Authors

1 Department of Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, India.

2 Department of Information Technology, Guru Ghasidas University (A Central University), Bilaspur (CG India).

3 Department of Computer Science and Engineering, Guru Ghasidas University (A Central University), Bilaspur (CG India).

10.22059/jitm.2025.102925

Abstract

This study aims to present a novel generative AI-driven system for hyper-personalized health monitoring. Dynamic data processing, predictive modeling, and flexible learning improve real-time health evaluations. By combining weighted feature aggregation, iterative least squares estimation, and selective feature extraction, the suggested strategy makes predictions that are more accurate while using less computer power. Abnormality detection methods like adaptive thresholding and Kalman filtering provide accurate health monitoring. Attention, gradient-based optimization, and sequence learning improve health trend forecasts as the model improves. Generative AI-driven wearables outperform conventional and AI-based alternatives in many key performance tests. These evaluations include prediction accuracy (94%), real-time monitoring efficiency (93%), adaptability (92%), data integration quality (95%), and system reaction time (90 ms). These devices are safer (96%), have longer battery life (32 hours), and are simpler, more comfortable, and scalable. The results suggest that creative AI can transform personal healthcare into something more adaptable, safe, and affordable. Generative AI-powered smart gadgets are the most sophisticated means to monitor health in real time and deliver individualized, data-driven medical treatment. Future research will concentrate on improving prediction models and developing AI-driven modification approaches to make them more effective in additional healthcare scenarios.

Keywords

References

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Journal of Information Technology Management
Volume 17, Special Issue
2025
Pages 130-154

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