Indices of embodied Neuroergonomic coupling: a theory and hypothesis framework for quantifying brain-body-environment dynamics in built space.

Background: Built environments shape navigation, attention, and motor control through continuous brain-body-environment coupling, yet architecture, rehabilitation, and clinical mobility practice still lack a shared quantitative language for this interaction. Architects lack quantitative feedback linking spatial decisions to neural or gait outcomes; clinicians rely on episodic assessments that capture capacity snapshots but not continuous coupling. Portable multi-modal sensing, ecological neuroscience, and computational frameworks now make this problem newly tractable. Objective: To propose a theory-driven, testable framework of indices that operationalize embodied cognitive-motor coupling without reducing embodiment to a unitary resource-depletion model. The framework adopts an embodied-enactive stance and separates perceptual selection load, control and coordination load, and arousal regulation as partially overlapping mechanisms with distinguishable temporal, spectral, and recovery signatures. Framework: Six linked indices span four conceptual layers. Spatial Cognitive Demand (SCD) quantifies environment-level demand from pattern complexity, luminance variance, transition density, and visual-tactile alignment. Cognitive-Motor Fusion Index (CMFI) integrates neural demand, gait control cost, and instability into a bounded composite. Neuroergonomic Efficiency Quotient (NEQ) indexes motor performance relative to neural demand. Balance Recovery Coefficient (BRC) quantifies perturbation recovery quality. Gait-Cognition Coherence (GCC) measures frontal-theta to gait-phase coupling with context-dependent interpretation. Cognitive-Motor Headroom (CMH) estimates distance to an individualized operating boundary. Each index includes variable definitions, normalization constraints, and quality-governance requirements. Core hypotheses: Eight falsifiable hypotheses are advanced spanning five domains: environment-dependent shifts in coupled demand, mechanism-specific temporal dynamics, context-dependent interpretation of neural-gait coherence, conditional cross-domain transfer through domain-general control processes, and cross-level prediction from environmental structure to longitudinal mobility outcomes. These include predictions that higher-SCD environments elevate CMFI and reduce NEQ with stronger effects in lower-reserve groups, that perceptual-load and control-load manipulations yield separable spectral signatures, that elevated GCC reflects reduced automaticity in low-demand but adaptive recruitment in high-demand contexts, and that a multi-factor latent structure fits better than a single undifferentiated load factor. Conclusion: This manuscript does not report an inferential human-subject dataset. Its contribution is a conceptual and mathematical scaffold with a five-phase validation roadmap, reporting requirements for mobile EEG artifact governance, and a structured template for cumulative empirical testing across laboratories and populations.