CEDRIC-J-MURAGE-MWANGI/SalusMaternaPublic

SALUS.MATERNA is a solar-powered, AI-driven vital signs monitor designed to reduce maternal mortality in rural Kenya. It tracks SpO₂, temperature, and cuffless blood pressure, using ML to detect risks like PPH and sepsis. Alerts are sent via SMS/call, with data streamed to Azure cloud. # SALUS.MATERNA — AI & Solar-Powered Maternal Health Monitoring System --- ## 🏥 Overview SALUS.MATERNA is a solar-powered, IoT-enabled maternal health monitoring device designed for rural Kenyan healthcare facilities. It was developed to address the critical infrastructure gaps that contribute to Kenya's maternal mortality rate of **355 deaths per 100,000 live births** — equivalent to roughly 16 women every single day. Rural facilities face three core, interconnected problems this system targets: 1. **No continuous monitoring** — intermittent manual checks every 4–6 hours miss early deterioration 2. **Frequent power outages** — blackouts render conventional electronic equipment non-functional 3. **No remote alerting** — understaffed clinics have no way to notify off-site clinicians of deteriorating patients SALUS.MATERNA addresses all three simultaneously through continuous vital signs monitoring, solar-powered off-grid operation, real-time cloud telemetry, and remote SMS/voice call alerts — combined with an ensemble machine learning model for early detection of maternal complications including PPH, sepsis, hypertensive disorders, and pulmonary embolism. --- ## ✅ Key Features - **Continuous vital signs monitoring** — Heart Rate, SpO₂, Temperature, and cuffless Blood Pressure - **Novel cuffless BP measurement** — Piezoelectric + PPG sensors along the radial artery, using Pulse Wave Velocity derived from the Moens-Korteweg equation and Beer-Lambert PIR model - **Solar-powered operation** — Full off-grid functionality via 6V PV panel and Li-ion battery - **Cloud telemetry** — Real-time data upload to Microsoft Azure at 60-second intervals - **Remote alerting** — Twilio SMS and voice calls delivered within 60 seconds of a detected risk - **On-device alerts** — OLED display, LED indicators, and buzzer for local notification - **Ensemble ML model** — Early detection of PPH, sepsis, hypertensive disorders, and pulmonary embolism - **Web dashboard** — Real-time …