Development of a Scalable Data Acquisition System for Multimodal Physiological Data for Human Factors Research

Abstract

This paper presents the development of a scalable data acquisition (DAQ) system designed for real-time collection of multimodal physiological data in human factors research. The system integrates various sensors such as EEG, ECG, eye tracking, and fNIRS into a unified framework using the MQTT protocol for asynchronous data transmission. Data are stored in InfluxDB, a time-series database, and visualized in real time through Grafana dashboards. The system enables flexible sensor management and real-time data processing through listener and processor clients, making it adaptable to different research needs. Validated through pilot flight simulations, the DAQ supports advanced modeling techniques such as Bayesian analysis for estimating cognitive workload in real time. Results show its ability to synchronize and compress high-volume data for inference on human performance. This framework offers significant potential for adaptive automation research by supporting real-time modeling and adjustment based on operator state.