AI-Enhanced Wearable Technology for Human Physiological Signal Detection: Challenges and Future Directions

Abstract

In the past decade, advances in IT, microelectronics, materials science, and the growing demand for new medical solutions in an aging society have greatly boosted wearable devices' ability to monitor physiological signals. However, traditional methods of physiological signal analysis have limitations when it comes to processing complex, multimodal data, particularly in the context of nonlinear, non-stationary, and highly personalized information. Recently, AI technologies-especially deep learning, machine learning, and multimodal data fusion-have introduced new solutions for physiological signal analysis, significantly improving the accuracy and real-time performance of signal processing. This work reviews the latest advancements in AI within the realm of wearable physiological signal monitoring. It systematically explores the advantages of AI in enhancing the accuracy of signal extraction and classification, enabling personalized health monitoring and disease prediction, and optimizing human-computer interaction. Additionally, it analyzes specific applications of AI in the analysis of bioelectric, mechanical, chemical, and temperature signals. The work also discusses challenges such as data privacy, algorithm generalization, real-time processing, and model interpretability. Finally, it prospects the development trends of AI-driven wearable physiological monitoring technology, focusing on materials, algorithms, chips, and multidisciplinary collaborative innovation.

Keywords: artificial intelligence (AI); health monitoring; multimodal data fusion; physiological signals; wearable devices.