From Heterogeneous Human Data to Verifiable Whole-Person Computational Relations: A Human-Function-Based Relational Specification and Cross-Disciplinary Evaluation Protocol

Abstract Different scientific domains—including medicine, physiology, rehabilitation, exercise science, nutrition, psychology, and environmental research—produce observations and models with distinct constructs, purposes, and evidentiary limits. Integrating these records does not automatically establish which conclusions are warranted about the same continuing person. This preprint proposes a human-function-based relational specification for transforming heterogeneous human data into verifiable, evidence-qualified whole-person computational relations. Grounded in the Unified Ontology of Human Function (UOHF) and the Human Function World Model (HFWM), the proposed framework explicitly distinguishes internal and external demands, available functional capacities, actual functional engagement, observations, consequences, contextual conditions, scientific reference knowledge, and professional actions. The methodology specifies typed cross-domain mappings, evidence-qualification requirements, four complementary computational relationship graphs, question-specific inference boundaries, and dependency-preserving longitudinal updates. It also defines a prospective evaluation protocol covering medicine, physiology, rehabilitation, exercise, nutrition, psychology, and environmental contexts. The central research hypothesis is that explicit demand–capacity–functional-engagement relations can improve evidence-warranted whole-person judgments and selective updating compared with equally informed, well-governed alternative computational representations. The manuscript presents a formal methodological specification, constructed competency cases, and falsifiable evaluation requirements. It does not claim completed independent empirical validation, calibrated human dynamics, clinical superiority, or autonomous treatment capability. The intended contribution is a reusable computational foundation for connecting heterogeneous scientific evidence about the same person while preserving the meanings, limitations, and independent validity of existing disciplinary models.