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Problem context
The client required a connected wearable device capable of collecting multiple physiological signals and transmitting them reliably under real-world conditions. Unlike consumer IoT devices, the system had to operate continuously, tolerate motion and environmental noise, and maintain predictable battery behavior while serving as a stable edge node in a larger connected system.
Constraints
- Battery-powered operation with limited energy budget
- Concurrent operation of multiple sensors (ECG, pulse, motion, pressure, temperature)
- Wireless data transmission without continuous connectivity
- Real-world motion and environmental interference
- Need for predictable edge-device behavior over long operating cycles
Engineering decisions
Decision: Redesign the device architecture around low-power edge operation
Reason: Treating the device as an always-on IoT node required strict control of power states and duty cycles.
Trade-off: Increased firmware and power-management complexity.
Decision: Implement staged sensor activation and adaptive sampling rates
Reason: Not all sensors require the same temporal resolution; adaptive sampling significantly reduced power draw.
Trade-off: Added complexity to sensor orchestration logic.
Decision: Use accelerometer data to gate physiological signal processing
Reason: Motion-aware data validation reduced noise and unnecessary processing at the edge.
Trade-off: Data availability became window-based rather than continuous.
Decision: Introduce a dual-processor architecture
Reason: Separating real-time data acquisition from analysis and communication improved system stability and power efficiency.
Trade-off: Higher BOM and architectural complexity.
Decision: Design a custom, bandwidth-efficient communication protocol
Reason: IoT systems benefit more from reliable, complete data transfer than real-time streaming.
Trade-off: Increased protocol design and validation effort.
System overview
The resulting solution is a low-power IoT wearable functioning as an edge computing node. It continuously manages sensor states, performs local signal preprocessing, and transmits verified data batches over a wireless connection. Power-aware firmware governs sleep, wake, and processing states, ensuring stable long-term operation without user intervention.
Outcome
Approximately 40% reduction in power consumption compared to initial architecture. Stable wireless data transmission with minimized bandwidth usage. Reliable edge-level preprocessing under motion conditions. Predictable battery behavior suitable for extended operation. IoT-ready architecture designed for scaling and future integration.
Engagement delivered under NDA. Details anonymized.