Two home-technology studies in neuro care advanced non-invasive monitoring approaches. KAIST researchers unveiled an AI system to detect early cerebrovascular disease signs at home using contactless sensors that track activity patterns, sleep quality, circadian rhythms, and indoor environmental factors. In a separate study, an npj Parkinson’s Disease extension examined long-term deep sleep modulation delivered via home-based technology, reporting feasibility and sustained effects in people with Parkinson’s. Together, the work points to a shift toward remote, continuous data capture—supporting earlier detection and potentially extending therapeutic options beyond clinic-based visits. Both approaches depend on behavioral signals as measurable proxies for disease-relevant physiology.