A Multimodal Affective Computing Framework for Non-Invasive Stress Detection and Regulation: System Architecture and Pilot Validation

Abstract: The United States faces a critical shortage of mental health providers, creating significant barriers to care for individuals experiencing sub-clinical distress. Traditional mobile health interventions often lack the engagement necessary to form a sustainable therapeutic alliance. This technical report introduces the Multimodal Affective Computing Framework (MACF), a novel software architecture designed to serve as a scalable, non-clinical “assistant” and "first responder." The framework utilizes generative AI to provide immediate coping mechanisms, reducing the burden on human providers. The system employs a Bimodal Interaction Logic (separating ambient behavioral feedback from active conversational interaction) to manage user cognitive load. We detail the implementation of a Priority-Based Audio Gating Protocol that prevents sensory overload during therapeutic interventions. Furthermore, we analyze a Multimodal WebSocket Pipeline that converts user drawings into real-time, token-streamed audio commentary, simulating the responsiveness of face-to-face interaction. The report outlines a "Fail-Open" safety strategy coupled with strict Usage Limit Services to prevent dependency and manage potential mania, ensuring the tool remains a wellness supplement rather than a clinical replacement. By combining low-latency on-device processing with haptic reinforcement, the proposed framework demonstrates how engineering optimizations can enhance the accessibility and efficacy of digital mental wellness tools.