IoT Product Engineering for Consumer Health: Cost-Optimized Design for Eye-Drop Counter with Edge AI Embedded Systems
Introduction: The Challenge of High-Cost Consumer Health Devices
Existing prototype costs with nRF52832 SoC and high-end IMU made the unit cost too high for large-scale consumer distribution. The client needed a significantly reduced BOM while retaining >75% detection accuracy and 90+ day battery life, demanding a highly cost-conscious IoT product engineering solution.
Solution Overview: Edge AI Embedded Systems with Cost-Optimized Sensing
EurthTech delivered a cost-optimized redesign by replacing expensive MCUs and sensors and simplifying the sensing algorithm. We moved from a 9 - axis IMU to a well-chosen 3 - axis accelerometer and replaced the power-hungry regulator with a low - Iq buck. The system uses a hybrid heuristic + compact ML model to preserve accuracy while functioning as a high-efficiency Edge AI embedded system.
Technical Implementation: Embedded Systems Development and Manufacturing Automation
The embedded systems development focused on ultra-low-cost Arm Cortex - M0 devices. The sensing algorithm uses a two-stage approach: a low-cost heuristic filter flags candidate gestures, and only then is a compact classifier (quantized decision tree) run on-device. Manufacturing and test automation included a ICT/test jig that validates accelerometer calibration and a production test that verifies detection logic using a synthetic motion sequence, ensuring predictable mass production.
Results & Impact: AI Engineering and Operational ROI
A 200-unit pilot achieved 76-81% detection accuracy after per-user calibration, meeting clinical utility targets. Average cost-per-unit BOM fell by ~48% versus the previous design. The redesign enabled a viable go - to - market price point for consumer channels and reduced factory commissioning time by 65%, providing clear operational ROI.
Scalability & GIS Context: We provided a vendor shortlist and a production test plan for EMS partners. The simplified firmware allowed a deterministic OTA package and one-step factory provisioning. This solution is a crucial component of consumer Predictive maintenance AI IoT platforms, demonstrating how cost-effective hardware can scale health and compliance monitoring across a distributed user base, aligning with the goals of a smart city solutions provider.