Precision Health Monitoring: Exploring the Fusion of Wearable IoT Sensors, Multimodal Data, and ML

Abstract

The current state of health detection in IoT sensors is affected by several limitations. Existing systems often struggle with inaccurate and unreliable readings, leading to potential misdiagnoses and patient discomfort. These systems also tend to lack adaptability and robustness when dealing with various modalities of data, hindering their overall effectiveness. Furthermore, the reliance on traditional algorithms in the absence of machine learning hampers their ability to provide precise and real-time heartbeat information. In light of these shortcomings, this work seeks to address these issues by studying the enhanced approach, emphasizing the integration of multimodal data fusion techniques and machine learning algorithms. The aim is to identify the drawbacks associated with existing systems and provide the more accurate and responsive solution for heath detection offered by IoT sensors through the application of data fusion and machine learning.