A Distributed Framework for Remote Multimodal Biosignal Acquisition and Analysis

In recent times, several studies have presented single-modality systems for non-contact biosignal monitoring. While these systems often yield estimations correlating with clinical-grade devices, their practicality is limited due to constraints in real-time processing, scalability, and interoperability. Moreover, these studies have seldom explored the combined use of multiple modalities or the integration of various sensors. Addressing these gaps, we introduce a distributed computing architecture designed to remotely acquire biosignals from both radars and cameras. This architecture is supported by conceptual blocks that distribute tasks across sensing, computing, data management, analysis, communication, and visualization. Emphasizing interoperability, our system leverages RESTful APIs, efficient video streaming, and standardized health-data protocols. Our framework facilitates the integration of additional sensors and improves signal analysis efficiency. While the architecture is conceptual, its feasibility has been evaluated through simulations targeting specific challenges in networked remote photoplethysmography (rPPG) systems. Additionally, we implemented a prototype to demonstrate the architectural principles in action, with modules and blocks operating in independent threads. This prototype specifically involves the analysis of biosignals using mmWave radars and RGB cameras, illustrating the potential for the architecture to be adapted into a fully distributed system for real-time biosignal processing.