Anonymo AI Framework (TCF/TFB) — Phase 0: Tool Recognition and Sensor Position Validation

This technical note defines Phase 0 of the Anonymo AI Framework (TCF/TFB), focusing on initial tool recognition, signal behavior assessment, and sensor position validation under real-world, non-controlled conditions. The primary objective of this phase is to evaluate the operational characteristics of a wearable system, including signal stability, consistency, positional sensitivity, and response to natural movement. No physiological inference or longitudinal interpretation is performed at this stage. Phase 0 is explicitly designed as a calibration and familiarization layer to ensure that data acquisition conditions are sufficiently stable and interpretable before initiating structured, long-term observation. The methodology adopts a minimal-control, real-world approach, prioritizing practical validation of signal reliability over controlled laboratory conditions. Variables such as heart rate (HR), oxygen saturation (SpO₂), and step count are observed solely in relation to sensor performance and not as indicators of physiological state. Sensor positioning is evaluated through comparative use of different placements on the wrist to determine the configuration that provides the highest signal continuity and lowest noise under typical usage conditions. In parallel to this phase, ongoing experimental efforts related to voice dynamics and pattern recognition have been conducted and continue to evolve within operational constraints. These developments are not directly included in this document but are part of the broader framework context. This publication does not contain personal data, does not expose third-party systems, and does not include identifiable information. It serves exclusively as a methodological baseline and a structural starting point for subsequent phases of the framework. Future phases will build upon this foundation, introducing controlled longitudinal data collection, multi-variable analysis, and integration within the broader Anonymo AI system architecture.