Cloud-Native Real-Time Health Monitoring with Bio-Robotic Sensors and MERN-Based Edge Analytics

Uma Ajay Kumar Reddy PS

Abstract: Continuous home health monitoring is increasingly feasible, yet most bio-robotic devices remain hardware prototypes with weak longitudinal data infrastructure. This paper proposes a cloud-native architecture that integrates bio-robotic sanitary sensors, edge gateways, and a MERN-based multi-tenant backend for real-time biochemical screening. Non-invasive sensors embedded in household fixtures capture urine, stool, and vital-sign signals under robotic alignment and cleaning control. MQTT and WebSocket transport deliver telemetry to Node.js and Express microservices, while MongoDB stores schema-flexible biometric events and Flask-based analytics services execute Random Forest and long short-term memory models for anomaly detection and trend forecasting. Unlike device-silo designs, the proposed stack treats robotic sensing, data engineering, and clinician-facing visualization as one end-to-end system. The paper discusses the design through a smart toilet case study and defines a literature-grounded operating target for early chronic-risk screening in home-centric care.

Keywords: Bio-robotic sensors, edge computing, MERN architecture, smart toilet, preventive health monitoring

How to Cite?: Uma Ajay Kumar Reddy PS, "Cloud-Native Real-Time Health Monitoring with Bio-Robotic Sensors and MERN-Based Edge Analytics", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 666-669, https://www.ijsr.net/getabstract.php?paperid=SR26310004056, DOI: https://dx.doi.org/10.21275/SR26310004056

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