Deterministic Edge Acquisition of Contextual, Multimodal Physiological Signals: A Bilateral-Ready Dual-Core IoT Biosensing Architecture for Ageing-Focused Research

Sensor capability has outpaced the architectures available to integrate it: in near-body multimodal sensing, the binding constraint is no longer whether a variable can be measured, but whether a platform coordinates heterogeneous channels while preserving timing integrity, contextual interpretability, and extensibility. The gap is acute in ageing-focused research, which needs both side-specific comparison and heterogeneous sensing. We present the Deterministic Embedded Acquisition Platform Biosensing Hub (DeAP-BioHub), a dual-core STM32H755 architecture isolating acquisition from variable-latency subsystems by peripheral ownership. A real-time Cortex-M4 domain performs scheduled sensing, timestamping, and record creation, and a bare-metal Cortex-M7 domain carries telemetry, file-system logging, and operator interaction; the two share only a non-cacheable region and hardware semaphores, exchanging fixed 464-byte records with build-time offset assertions. With telemetry and logging active, the environmental channel held its nominal period at 1000.000 ms, at 1 ms timestamp resolution (SD 0.188 ms, n = 452), 96.5% exactly at nominal; the infrared channel’s 0.517% excess traced to that sensor’s own refresh cycle rather than the scheduler, and no storage-synchronization signature was detectable in the four sessions (combined Rayleigh test, p = 0.99). Contextual sensing shares that scheduling, record structure, and timestamp source rather than sitting in metadata: a fingertip registration was summarized descriptively by a first-order approach (τ = 154 s, R2 = 0.997), and a session reading flat on infrared alone stayed diagnosable from the environmental record. Ageing-focused research is the target application: this is laboratory verification of the acquisition substrate, not evaluation in older adults or clinical validation.