Device Architecture and Sensor Selection
- Choose low-power microcontrollers and sensors tailored for wearable applications. Opt for components that support low-energy modes and efficient communication protocols like BLE (Bluetooth Low Energy).
- Consider using sensors with built-in processing capabilities (smart sensors) to offload computations from the main processor.
- Implement sleep modes aggressively. Ensure that peripherals are turned off when not in use and wake up the main processor only when necessary.
Power-Efficient Coding Practices
- Optimize your firmware for efficiency. Avoid busy-wait loops and prefer event-driven architectures to minimize processor wake-up times.
- Utilize Direct Memory Access (DMA) where possible to reduce CPU load during data transfers.
- Use hardware timers instead of software-based timing mechanisms for precise power gating.
- Implement dynamic clock scaling to adjust the processor speed based on workload requirements.
Energy Harvesting and Management
- Explore available energy harvesting options like solar cells or thermoelectric generators to extend battery life.
- Manage harvested energy with efficient power management ICs (PMICs) that can deal with varying power inputs and intelligently charge the battery.
Communication Optimization
- Reduce unnecessary data transmissions. Use data compression techniques and batch small data packets to send larger but less frequent messages.
- Optimize network stack for device environments. For BLE, utilize connection intervals effectively to maximize power retention.
- Whenever feasible, use sub-GHz radios as they often have better range and lower power consumption for certain applications.
Algorithm Optimization
- Profile your software to identify energy-intensive computations. Optimize these areas using fast Fourier transforms (FFTs) or similar techniques if applicable.
- Utilize approximate computing techniques where precision can be traded for power reduction.
- Make use of specialized neural network chips (Edge AI) for tasks involving machine learning for lower power usage compared to general-purpose CPUs.
Battery and Power Supply Design
- Choose energy-dense batteries with high discharge capabilities. Balance between size, weight, and capacity to suit your wearable device.
- Implement active power monitoring to ensure battery health and to make runtime adjustments based on power availability.
- Design power supplies with high efficiency DC-DC converters for efficient voltage regulation.
Prototyping and Testing
- Set up power measurement equipment like oscilloscopes or specialized power profiling tools to monitor exact power usage.
- Run extensive field trials under various environmental conditions to simulate real-world usage and optimize power consumption further.
