Embedded AI India: AI-Powered Embedded Systems for Predictive Maintenance AI IoT in Cardiac Screening
Introduction: The Challenge of Signal Quality and Latency
Affordable, ambulatory monitoring often lacks the signal quality or analytics required to detect subtle HRV and arrhythmia signatures. Relying exclusively on cloud processing increases latency, data transfer costs, and presents privacy concerns. The challenge was to deliver a battery-optimized smart infrastructure solution capable of clinical-grade analysis at the source, driving efficiency in IoT product engineering.
Solution Overview: Edge AI for Ambulatory Cardiac Screening
The PremAsh pulse-oximeter is an AI-powered embedded system designed for sleep and cardiac screening. It utilizes on-device ML for apnea/arrhythmia detection and HRV analysis. By running a classifier locally, the device minimizes raw data transfer, uploading only summary events and secure excerpts for clinician review. This approach serves as a core component of a Predictive maintenance AI IoT strategy for personal health.
Technical Implementation: Embedded Systems Development and Edge AI
The device architecture, led by our Embedded AI India team, combines a high-SNR photodiode LED front-end and a low-noise AFE with a small MCU utilizing DSP instructions. Core analytics include robust pulse detection, HRV feature extraction, and arrhythmia heuristics. The key is the onboard classifier (quantised CNN + rule-based fusion) that combines SpO2 desaturation patterns with reduced HRV signatures to flag likely apnea events. This represents a highly efficient embedded systems development focused on multi-mode operation for power balance.
Results & Impact: AI Engineering and Smart City Solutions
Bench validation against clinical pulse-ox and ECG references confirmed signal integrity. The device’s Edge ML lowered data transfer costs and significantly reduced clinician review time, opening a recurring revenue stream for cloud analytics and teleconsultation—a direct result of our AI engineering solutions. This low-cost, high-value architecture provides a blueprint for any smart city solutions provider looking to roll out mass-market health monitoring.
Scalability & GIS Context: Designed with a traceable manufacturing and clinical validation pathway, the device is ready for consumer and regulatory workflows. While primarily medical, its deployment at scale requires robust asset tracking and management, leveraging skills typically provided by a GIS consulting company to manage inventory and ensure compliance across distributed healthcare smart lighting systems and facilities.