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Yusaku
Fujii, Ph.Dbｂ |
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Professor, Gunma
University (Japan) President, (NPO)
The e-JIKEI Network Promotion Institute (Japan) |
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PAPR for Everyone: Toward a Wear-Rate Management
Network That Unifies Pandemic Control and Privacy Protection - Why VRAIO-Based Verifiability Is the Essential
Infrastructure |
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As an
engineering response to airborne pandemic threats, the presenter has proposed
the concept of universal PAPR deployment for the general public [PAPR for Everyone: Foundational Concept and Feasibility].
The central finding is that the effective reproduction number However, the situation changes
qualitatively if the inherent sensor capabilities of PAPR——airflow
characterization via differential pressure and PWM signals [PAPR Fluid Model: Airflow via Differential Pressure and
PWM], and real-time estimation of
respiratory flow and mask leakage [PAPR Fluid
Model: Real-Time Respiratory Flow and Leakage Estimation]——can be centrally aggregated via smartphone interfaces. Sensor data
generated by citizens at the scale of 100 million in Japan alone would
function as an AI-driven big data infrastructure, enabling real-time
monitoring, prediction, and intervention in infection dynamics far beyond
what conventional contact-tracing applications could achieve. In a
sensor-dense society, such AI-driven public safety infrastructure constitutes
a new cornerstone of pandemic engineering. At this point, we must return
to a foundational principle of engineering and science: "No measurement,
no understanding; no understanding, no control." Insufficient
measurement yields only insufficient understanding. Control built upon
insufficient understanding is, however carefully designed, inevitably
insufficient in turn. In light of this principle, the construction of a
system grounded in precise measurement and management of wear rates is an
indispensable condition for rendering infection control genuinely worthy of
the name [PWS-NET: Wear-Rate Management for
Infection Control and Civil Liberties]. Yet this comes with a serious
problem. The information collected by PAPR——wear history, behavioral history, and real-time
respiratory status——constitutes an aggregation of
highly sensitive personal data. When an AI-driven public safety
infrastructure operating at the scale of 100 million citizens accumulates
biometric and behavioral information, the trustworthiness of that system must
be beyond question. Indeed, the fundamental reason why contact-tracing
applications around the world failed to collect sufficient measurement data
during COVID-19 was not technical limitation, but the social reality that
citizens could not trust the systems. Trust in public safety infrastructure
is not generated by declarations of good intent; it can only be guaranteed by
a verifiable architecture. The VRAIO (Verifiable Record of
AI Output) infrastructure proposed by the presenter is positioned as
precisely the answer to this problem [VRAIO:
First Proposal, with FMPS as Target]. Through a three-layer
architecture comprising tamper-evident output records, spot-check auditing,
and asymmetric penalty structures, VRAIO institutionally guarantees
accountability and the protection of civil rights in AI-involved systems——giving concrete
infrastructural form to the principles of Trustworthy AI, rather than leaving
them as mere declarations. The efficiency of infection control and the
protection of individual rights are not an unavoidable
trade-off. The establishment of a VRAIO-based privacy protection
infrastructure is the indispensable condition for elevating PAPR for Everyone
into a pandemic engineering framework that is truly ready for societal
implementation——and this lecture presents the case
for that claim. References [PAPR for Everyone: Foundational Concept and Feasibility] Y. Fujii, “An Engineering
Alternative to Lockdown During COVID-19 and Other Airborne Infectious Disease
Pandemics: Feasibility Study”, JMIR
Biomedical Engineering, Vol.9, e54666, 2024. https://biomedeng.jmir.org/2024/1/e54666/ [PAPR for Everyone: Controlling Rt via Population Wear Rate] Y. Fujii, “Examination of
the requirements for powered air-purifying respirator (PAPR) utilization as
an alternative to lockdown”, Scientific
Reports, Vol.15, 1217, 2025. https://www.nature.com/articles/s41598-024-82348-0 [PAPR Fluid Model: Airflow via Differential Pressure and PWM] Y. Fujii, A. Takita, S. Hashimoto and
K. Amagai, “Estimation of Respiratory States Based on a Measurement Model of
Airflow Characteristics in PAPR Using Differential Pressure and PWM Control
Signals: In the Development of a Public-Oriented PAPR as an Alternative to
Lockdown Measures”, Sensors, Vol.25, No.9,
2939, 2025. https://doi.org/10.3390/s25092939 [PAPR Fluid Model: Real-Time Respiratory Flow and Leakage
Estimation] Y. Fujii, “The Real-Time Estimation of Respiratory Flow and
Mask Leakage in a PAPR Using a Single Differential-Pressure Sensor and
Microcontroller-Based Smartphone Interface in the Development of a
Public-Oriented Powered Air-Purifying Respirator as an Alternative to
Lockdown Measures”, Sensors, Vol.25, No.17,
5340, 2025. https://doi.org/10.3390/s25175340 [PWS-NET: Wear-Rate Management for Infection Control and Civil
Liberties] Y.Fujii, “Time-managed PAPR use
enables a balanced approach to infection control and personal freedom”, Scientific Reports, Vol.16, 2878, 2026. https://doi.org/10.1038/s41598-025-32625-3 [VRAIO: First Proposal, with FMPS as Target] Y.
Fujii, “Verifiable
record of AI output for privacy protection: public space watched by
AI-connected cameras as a target example”, AI
& Society, Vol.40, pp. 3697–3706, 2025. https://doi.org/10.1007/s00146-024-02122-8 |
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