Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Signal Processing

Background:

• Wireless body sensors for continuous physiological monitoring, like electrocardiograms (ECG), are crucial for remote healthcare.
• Sensor autonomy is limited by battery life, influenced by data transmission and measurement quality.
• Bluetooth Low Energy (BLE) is a common communication protocol for these low-power devices.

Purpose of the Study:

• To analyze the power consumption of a wireless ECG sensor.
• To develop analytical models for predicting sensor autonomy.
• To identify key parameters for optimizing energy efficiency in long-term ECG monitoring.

Main Methods:

• In-depth analysis of power consumption sources, focusing on BLE communication.
• Development of two analytical models: one for idle mode and one for active mode power consumption.
• Validation of models using measured power consumption data across various ECG sensor settings (sampling rate, transmit power).

Main Results:

• Proposed power consumption models accurately predict sensor behavior across different sampling rates.
• Transmit power has a minimal impact on autonomy when streaming high-rate ECG data.
• Significant energy savings are achievable by reducing the sampling rate and optimizing data packet size for BLE.

Conclusions:

• The developed analytical models provide a reliable method for predicting wireless ECG sensor autonomy.
• Optimizing sampling rate and data packing in BLE packets are key strategies for extending sensor operational time.
• This research enables enhanced long-term ECG monitoring through improved power management of wearable sensors.