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.