Key result
H-pipeline architecture delivers ~18,000x token reduction on ECG data while lowering patient re-identification risk.
Effect estimate: 17,999× token reduction
p-value: p=2.23 × 10⁻¹³
The H-pipeline architecture provides massive token reduction and privacy protection for bioelectric and kinematic sensor streams, enabling efficient IoT-to-LLM integration.
May support private ECG-to-LLM pipelines; leaves open prospective clinical validation before adoption.
This deposit contains the complete pre-registration corpus (embedded in section-batch runner scripts), runner code, summary verdicts, per-study reports, figures, POC addenda, and arXiv-format manuscript for a 259-study pre-registered empirical validation campaign of a universal Internet-of-Things to large-language-model (IoT-LLM) pipeline architecture across bioelectric, kinematic, and intraoperative-surgical sensor streams. The campaign comprises approximately 271 pre-registered hypothesis-testing decision rules across twenty-one thematic sections, executed under continuous deterministic protocol (PYTHONHASHSEED=0) with zero post-hoc threshold adjustments. The campaign achieves 91.5% SUPPORTED (248 of 271 hypotheses). The architecture under test (the *H-pipeline*) comprises a spiral-domain encoder, a privacy-mechanism-protected k-means codebook quantization layer, a large-language-model byte-pair-encoding tokenizer (tiktoken cl100k_base), a two-layer privacy composition rule, and per-cohort operating-point calibration, decomposed into twelve stages H-A through H-L. Key empirical findings: (i) 17,999× token reduction on real MIT-BIH Arrhythmia Database electrocardiogram data (Mann-Whitney U p = 2.23 × 10⁻¹³, bootstrap 95% confidence interval lower bound 17,999×); (ii) codebook quantization is empirically privacy-bearing on real PhysioNet data — capable-adversary patient-re-identification AUC reduces by Δ = 0.461 (from 0.952 raw-feature baseline to 0.491 post-codebook); (iii) Stage H-K kinematic privacy mechanism composition rule generalizes the pipeline to real KIMORE rehabilitation skeleton (2,699× token reduction) and real CMU MoCap walking (299× token reduction); (iv) Stage H-L intraoperative clinical-advantage triple-composition (Phase XXII + XXIV + XXV) executes at 83 ms p99 end-to-end within the 100 ms generic surgical clinical-decision-support budget and 1.15 ms p99 inner-loop within the 50 ms tight da Vinci surgical-robot control-loop budget, with workflow phase recognition AUC 0.970, adverse-event detection AUC 0.672, and multi-modal bioelectric-plus-kinematic fusion AUC 0.583. The campaign extends companion Papers 8, 9, 10, and 11. Foundational for U.S. Provisional Patent Application No. 64/080,096(Parent H, filed June 1, 2026). All input data are publicly available from PhysioNet (https://physionet.org), KIMORE (publicly distributed), and CMU MoCap (http://mocap.cs.cmu.edu). Released under CC-BY 4.0.
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Ferlic et al. (2026) studied Bioelectric, kinematic, and intraoperative-surgical sensor streams. H-pipeline architecture vs. Raw-feature baseline was evaluated on Token reduction on MIT-BIH Arrhythmia Database electrocardiogram data (17,999× token reduction, p=2.23 × 10⁻¹³). The H-pipeline architecture achieved a 17,999× token reduction on MIT-BIH electrocardiogram data (P=2.23×10⁻¹³) and reduced patient re-identification AUC from 0.952 to 0.491.
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