The cybersickness prediction work demonstrates the pattern cleanly: researchers trained models on synchronized EEG and head-mounted camera data from 24 participants, then used multi-model alignment to distill the EEG-informed predictions into a vision-only system that runs at 89% accuracy with 10ms latency on consumer VR hardware. The breakthrough isn't the prediction algorithm—it's engineering a deployment path that achieves clinical-grade performance without requiring users to wear invasive sensors. Similar constraints drive the in-vehicle language model compression (fitting 7B parameters into 2.3B while maintaining 95% function-calling accuracy for automotive compute budgets), the IMU activity recognition system (mining task-aware domains from non-i.i.d. sensor data for personalized inference without cloud connectivity), and the prosthetic vision optimization (discovering that checkerboard electrode activation outperforms row-by-row scanning, contradicting a decade of implant design assumptions). These aren't incremental improvements—they're solutions to the deployment problems that kept earlier research from shipping.
But hardware constraints are only half the story. The sepsis prediction system embeds clinical calculators directly into its temporal graph architecture because doctors won't trust black-box risk scores—they need familiar reference points like SOFA and qSOFA to verify outputs against their clinical judgment. The LLM-powered qualitative analysis workflow similarly scaffolds researchers by requiring explicit codebook validation and human review at each analysis stage, recognizing that trustworthiness in interpretive research depends on transparency and reflexivity, not just accuracy. The AI auditing platform structures collaboration between end users and practitioners through scaffolded workflows that translate lived experience into actionable insights. Across healthcare, research methods, and content moderation, the pattern holds: systems fail when they ask experts to trust outputs they can't verify within existing professional workflows.
The accessibility work reveals a third constraint: sensory substitution requires task-specific perceptual optimization, not general-purpose translation. The math equation editor discovers that spatial audio rendering of mathematical structure enables faster comprehension than linear text-to-speech. The AR captioning interface for deaf students requires real-time customization of caption positioning, size, and persistence based on individual reading speeds and classroom contexts. The prosthetic vision study shows that electrode activation patterns must be optimized for specific visual tasks—object recognition versus mobility versus reading. These findings challenge the assumption that faithful information encoding guarantees effective perception. The design question isn't whether to substitute modalities, but how to engineer perceptual clarity for specific tasks under specific constraints.