Human Function World Model

The problem HFWM addresses

Many systems serving a single individual operate separate domain models. Physiological measurements appear in one system; diagnoses and interventions in another; movement, exercise, and rehabilitation in others; and daily behavior or wearable observations elsewhere. Interfacing those databases can transfer information without explaining what it means for the person's actual function.

HFWM asks a deeper question: How do internal life processes support or limit external tasks? How do disease and medical interventions change available capacity and conditions of engagement? What observations warrant a specific inference? Why does the same task yield different outcomes over time? The goal is not simply more integrated data but a common computable relationship between evidence and personalized judgments.

Human function is the organizing principle; the whole person is the model's object

These levels must not be conflated. Human function supplies a coordinate system for relationships between demands, capacity, actual engagement, and consequences. The object continuously modeled by HFWM is the whole person: physiological mechanisms, anatomical structures, health and disease, clinical events, cognition, behavior, lived tasks, goals, social and environmental conditions, and longitudinal history. Different domain models may explain different aspects, while human-function relations provide a research framework for coordinating their relevance to the same individual.

HFWM is not merely a rehabilitation model at larger scale, not a movement-performance predictor alone, and not a claim to possess a complete validated simulation of a human being. It is a proposed computational framework for connecting domain results to judgments about the person.

Four questions that must remain connected over time

Where is this person now? Represent the current state and the evidence that can be confirmed. Why might things be this way? Relate demands, capacities, engagement, disease, physiology, and context to competing explanations. What should happen next? Identify the most useful further assessment, evidence, or professional action under appropriate authority and safety constraints. What might happen afterward—and what actually happened? Keep conditional forecasts separate from observed outcomes, then use subsequent evidence to test and revise earlier judgments. These questions organize the research agenda; they do not claim a fully operational autonomous clinical decision system.

From demands to consequences—and back toward explanations

Five distinguishable relationships organize this research:

Demand → Capacity → Engagement → Observed Consequences → Feedback and Update.

Demand concerns the requirements of internal regulation or a real-world task. Capacity concerns resources available under specified conditions. Engagement asks whether and how those capacities are actually mobilized. Observed consequences include performance, symptoms, costs, successes, and failures. Feedback supplies new evidence for subsequent understanding and action.

Reasoning must work in both directions. From a task, one may decompose requirements and investigate capacities or engagement constraints. From pain, altered movement, or performance decline, one may generate competing hypotheses and select assessments capable of distinguishing them. Knee pain does not, on its own, establish a disorder of a named muscle, patellofemoral structure, or nerve. Insufficient evidence must remain explicitly unknown.

Incorporating physiological, health, and disease models

Physiological models may help represent biological control, organ systems, and responses to demand. Health models can help organize overall state, recovery, and risk. Disease models can contribute diagnoses, progression, restrictions, and treatment context. Anatomy provides structural relations; movement and capacity models represent actions and conditions; cognitive and behavioral models provide relevant choice and engagement factors; environmental models provide external constraints.

HFWM does not propose to replace these models. It studies how local evidence or inferences from different models could be connected to what this person is trying or required to do, what capacities are available, how they are used, and what changes next, while retaining provenance, timing, validity, and uncertainty. Comprehensive multiscale implementation is an ongoing research goal, not a claim that all these models are already integrated and operational.

Distinguishing observations, inferences, futures, and outcomes

Observed facts may come from assessments, clinical histories, measurements, service records, or devices. Current inferences explain a person's state under stated evidence. Candidate futures are conditional possibilities, not historical facts. Observed consequences after a task or intervention create new evidence for updating the model. These categories require explicit identity, time, version, evidence, and uncertainty; AI-generated explanations cannot silently become confirmed diagnoses or permanent records.

How HFWM's contribution can be evaluated

The scientific claim is testable. With the same information and predefined tasks, HFWM can be compared with domain-specific rules, generic conversational AI, or conventional record aggregation for quality of state interpretation, separation of capacity and engagement, missing-evidence recognition, traceability, temporal updating, consistency, and professional workflow performance. Where appropriate and approved, evaluation should investigate whether these improvements generalize to real professional use and safety outcomes.

Research hypotheses, working software, demonstrations, and independent evaluations must be labeled separately. Superiority over existing methods cannot be claimed without comparative evidence.

Toward reusable whole-person computation and interoperability

Regardless of algorithm or domain model, does computing about the whole person require explicit functional relations to transform local model outputs into justified personal judgments? HFWM places this question at the center of its research program. If supported through comparative and independent validation, such relations may contribute to a reusable whole-person computational specification—not just a single implementation. A longer-term aim is a human-function protocol through which authorized clinical, rehabilitation, movement, device, and personal AI systems can exchange semantically clear functional information. No internationally adopted or mandatory protocol is claimed to exist today.