TCF/TFB 24x24 Framework Applied to Behavioral Pattern Monitoring via Wearable and Robotic Integration

Title: Multi-Age Behavioral and Biometric Capture Using Wrist-Based Sensors within the TCF/TFB Framework Note: This work was produced with the support of digital tools and artificial intelligence, taking into account the author’s physical limitations, including 95% vision loss in the left eye due to a recent cornea transplant caused by a bacterial infection. Since the first record on 19/12, the AI has been extensively used as a tool to assist in writing, image generation, and system design. Minor typographical errors or visual distortions may be present, which reflects the natural imperfection of the process. This also demonstrates that the system functions effectively even under these conditions. This work demonstrates the integration of wrist-based sensors and biometric monitoring across multiple age groups—from infants to adults and the elderly—within the TCF/TFB governance framework (Blueprint 24x24). The system captures vocal patterns, including infant crying, physiological signals (heart rate, movement), and interaction metrics in controlled modules and submodules. Data is codified anonymously and ethically, allowing longitudinal observation of behavioral and cognitive patterns while preserving user choice and privacy. The multi-level setup ensures the system is effective for: Monitoring developmental or behavioral trends across age groups Integrating data with cognitive training, neuroplasticity, and mental health modules Supporting professional oversight without creating invasive or continuous surveillance Establishing a framework that can interface with external AI systems or robotic modules for enhanced safety and consistency Web pags: Theory / Framework: https://tfbtheory.com Field Study / Anonymo App: https://www.anonymoai.org