Structural safety without unauthorized biometric replication

LAKANA Sovereign Structural Intelligence is a privacy-preserving safety framework for athletes, students, first responders, industrial workers, construction crews, logistics teams, tactical trainees, and other people whose bodies carry high physical load. This white paper explains how safety systems can help identify structural strain, heat stress, recovery collapse, gait changes, workload burden, and other physical risk signals without turning the person into a permanent biometric dataset owned by an institution or vendor. The paper introduces a governed access model where the individual keeps control of raw body-signal data by default, licensed medical or safety personnel may receive deeper data only through authorized and auditable access, and coaches, supervisors, or institutions receive bounded safety summaries rather than individual raw biometric details. It also presents simulation-bound evidence across five sport profiles, including women’s sports, and explains how the same privacy-preserving safety architecture can later be evaluated for load-bearing workforce settings. The white paper does not claim medical diagnosis, return-to-play authority, field validation, legal immunity, or regulatory clearance. It is a public commercial and technical white paper for controlled pilot diligence, institutional review, and partner evaluation.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-11
DOI
https://doi.org/10.5281/zenodo.20128643
Primary Topic
Gait Recognition and Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Structural safety without unauthorized biometric replication

martaize Fails
Zenodo (CERN European Organization for Nuclear Research)
Gait Recognition and Analysis
article

Structural safety without unauthorized biometric replication

martaize Fails
article en

Abstract

LAKANA Sovereign Structural Intelligence is a privacy-preserving safety framework for athletes, students, first responders, industrial workers, construction crews, logistics teams, tactical trainees, and other people whose bodies carry high physical load. This white paper explains how safety systems can help identify structural strain, heat stress, recovery collapse, gait changes, workload burden, and other physical risk signals without turning the person into a permanent biometric dataset owned by an institution or vendor. The paper introduces a governed access model where the individual keeps control of raw body-signal data by default, licensed medical or safety personnel may receive deeper data only through authorized and auditable access, and coaches, supervisors, or institutions receive bounded safety summaries rather than individual raw biometric details. It also presents simulation-bound evidence across five sport profiles, including women’s sports, and explains how the same privacy-preserving safety architecture can later be evaluated for load-bearing workforce settings. The white paper does not claim medical diagnosis, return-to-play authority, field validation, legal immunity, or regulatory clearance. It is a public commercial and technical white paper for controlled pilot diligence, institutional review, and partner evaluation.

Zenodo (CERN European Organization for Nuclear Research)
Lakka (Finland) (FI)
Gender equality
Openalex Percentile: Top 10%
Gait Recognition and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.