NSN Humanophotogrammetry Behavioral Model: Mapping Perceptual Error Through Photo Biological Time Based on a Photo-Temporal Framework of Perceptual Error in Human Action Analysis

This study introduces the NSN (NANDHA SATH NIYOG) Humanophotogrammetry Behavioural Model, a novel framework integrating 3D motion capture, machine vision, and cognitive neuroscience to quantify perceptual error (ΔP) in behaviour observation. Grounded in phenomenology (Merleau-Ponty, 1945) and embodied cognition (Varela et al., 1991), the model distinguishes digital error (ε_d: hardware limitations) from temporal illusion (ε_t: neurocognitive latency). A pilot study (N = 10) recorded participants during baseline and stress tasks using stereophotogrammetry (60fps) and synchronized EEG.  Results revealed:  ΔP ranges of 350–500 m s under stress (22%time dilation vs. objective timestamps, *p* < .05),  16% gesture misclassification in high-motion frames (ε_d), and  There was a 31% improvement in intent-action alignment after correcting Photo Auto Perception (PAP). The findings empirically validate that perception is time bound, challenging classical behaviourism. Applications span clinical diagnostics (e.g., anxiety via micro-expression latency) and human-AI interaction (temporal synchrony calibration). The study advances interdisciplinary dialogue by formalizing perceptual error as ΔP = ε_d + ε_t, bridging psychology, computer vision, and philosophy of mind. This paper introduces Humanophotogrammetry, a behavioural model quantifying human actions through photogrammetric data, anchored in the Theory of Photo Auto Perception (PAP). PAP posits that "accuracy of perception is the methodological error in data and illusion of reality of biological time sense", challenging classical psychophysical assumptions. We present a framework where behavioural metrics (e.g., gaze, posture) are extracted via 3D imaging and machine perception, then mapped to cognitive states. Clinical diagnostics and human-robot interaction applications are discussed, with validation pathways addressing PAP’s implications for empirical realism.  Highlights  Introduces Photo Auto Perception (PAP) theorem linking phenomenology and machine vision.  Quantifies perceptual error (ΔP) via EEG photogrammetry synchronization.  Demonstrates a 22% time-dilation effect under stress.  Open-source tools (Open Pose, Blender) enhance reproducibility.