Medical Engineering Defense, Samuel Solomon

Artificial intelligence continues to support our daily decision-making tasks yet remains disconnected from our dynamic emotions driving these behaviors. Wearable technologies can supplement with continuous emotion biofeedback, but existing models struggle to generalize across emerging biomarkers, platforms, and affective expressions. Here, we introduce a meta-analysis into embedding concurrent fragmented biosignals across 15 medical platforms, spanning five bodily locations, within a single profile that enables efficient generalizable downstream affective analysis. We achieve this through a Lie manifold neural architecture that simultaneously reconstructs over 118,000 missing biometric details in 205 biomarkers and accurately forecasts 100 affective states across populations, questionnaires, and activities. We validate this framework across five datasets and propose a new skin-conformal, soft bioelectronic, affective computing platform that demonstrates closed-loop emotion modulation with thermal, audio, and visual interventions delivered through virtual, holographic, and conversational agents. This work establishes a new foundational bidirectional architecture for scientific computing, offering interpretable, scalable, and emotionally responsive frameworks for the next generation of intelligent systems.