PainVector: A Multidimensional Computational Framework for the Digital Representation of Pain
Abstract
Pain is a multidimensional clinical phenomenon influenced by biological, psychological, behavioral, and contextual factors. Its assessment remains challenging because pain is a personal experience that cannot be directly observed and because commonly used clinical instruments, such as the Numeric Rating Scale (NRS) and Visual Analog Scale (VAS), primarily reduce pain to a unidimensional scalar value. Physiological signals such as heart rate variability (HRV), electrodermal activity (EDA), and peripheral temperature may provide complementary information, but these signals are themselves nonspecific and require contextual interpretation. We propose PainVector, a computational framework for representing pain as a structured multidimensional state rather than as a single scalar intensity value. The framework integrates self-reported pain, normalized physiological signals, and contextual variables including age, cultural context, and individual pain-expression characteristics. PainVector separates perceived pain from physiologically inferred pain and subsequently combines these components through an explicit fusion mechanism. The framework additionally incorporates temporal and system-level dimensions, including pain duration, predictability, controllability, localization, semantic interpretation, and confidence. A mathematical formulation is provided for signal normalization, contextual modulation, functional weighting, multimodal fusion, temporal prediction, and controllability…
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