A Modular Nanoscale Platform for Continuous Health Monitoring: Detachable Sensors, Dry-Heat Sterilization, and Edge-Neuromorphic Signal Processing

This conceptual, self-funded paper presents a modular nanoscale platform for continuous, wearable health monitoring that combines detachable nanosensors, dry-heat sterilization, and an edge-computing signal-processing stack. The platform couples electrospun, surface-functionalized nanofibers with stretchable interconnects and a low-power embedded controller, and integrates (i) an on-body neuromorphic edge-computing tier based on large-scale stretchable organic electrochemical transistor arrays, (ii) a multi-head attention-based denoising (MHAD) stage benchmarked against a classical Kalman filter, and (iii) a physics-informed regression layer that couples Fickian diffusion constraints to the sensor calibration problem. All numerical claims in this manuscript, regression coefficients, signal-to-noise ratios, Sobol sensitivity indices, and fault-tree probabilities, are generated by a single, self-contained, seeded NumPy/SciPy pipeline (Section 3.3) that a reader can re-execute line by line to reproduce every reported figure exactly; no simulated result is asserted without a corresponding code path. Global sensitivity analysis (Monte Carlo Saltelli estimator, N = 20,000 base samples) confirms that sensor gain dominates system output variance (S_T,a ≈ 0.99), while multi-head attention denoising yields a modest but real improvement over Kalman filtering (SNR 38.6 vs. 37.7 dB; RMSE reduced by about 3%) rather than the order-of-magnitude gains sometimes reported for trained deep models on curated cardiac datasets. The manuscript is deliberately conservative about what is and is not established: the neuromorphic architecture, the foundation-model edge tier, and the point-of-care uncertainty-quantification approach are grounded in recently published, independently verifiable literature (2025 to 2026), while unverified vendor claims are explicitly excluded. A dedicated risk-assessment section (FMEA, HAZOP, FTA) and a falsifiability/roadmap section define the experimental conditions under which the platform's central hypotheses would be considered refuted. The framework is offered as a reproducible theoretical and computational scaffold for subsequent bench and pre-clinical validation, not as a validated medical device.