BicycleSense360: Real-World Multi-Modal Naturalistic Cycling Data Acquisition and Synchronization System

Cycling, a sustainable active transportation, offers significant health, environmental, and societal benefits. Despite these advantages, cyclists are among the most vulnerable road users, with high fatality rates and many single-bicycle crashes (SBC) going unreported. Progress in cycling safety research is limited by the absence of comprehensive, real-world datasets capturing the interplay between cyclist physiology, bicycle motion, environmental perception, and contextual information. To address this, we present BicycleSense360, a modular, scalable, and real-time data acquisition system (DAS) for e-bicycles that unifies multi-modal sensing and is capable of supporting edge inference. The platform integrates synchronized data streams across five domains: (i) environmental perception; (ii) bicycle motion and motor telemetry; (iii) cyclist physiology; (iv) contextual information; and additionally includes a fifth domain v) bidirectional cyclist feedback, which supports real-time alerts and subjective experience logging. All sensors are orchestrated through a lightweight, socket-based publisher-subscriber architecture running on a Micro-controller unit. Unlike prior systems limited to isolated sensing or post-hoc analysis, BicycleSense360 ensures tight temporal alignment across data streams using a Last-In-First-Out (LIFO) truncation heuristic. We demonstrate synchronized multi-modal data collection under representative naturalistic cycling condition and visualize signal alignment across four domains.