研究目的
To design, prototype, and develop a novel wearable cardiorespiratory monitoring system called Piezologist that uses a single piezoelectric sensor to extract multiple parameters including heart rate, respiration rate, ECG waveform, and blood pressure, and to validate its performance against commercial devices.
研究成果
Piezologist is a viable wearable system for monitoring cardiorespiratory parameters using a single piezoelectric sensor, validated against commercial devices. It shows high usability and potential for widespread adoption in home healthcare. Future work should include broader subject testing and disease-specific assessments.
研究不足
The study was conducted on a limited number of subjects (six for performance comparison, fifteen for usability), and further experiments are needed to assess the algorithm over more subjects and compare with patients having cardiorespiratory diseases. The sensor packaging design affected signal strength and size, indicating potential for optimization.
1：Experimental Design and Method Selection:
The system uses a piezoelectric ceramic sensor (PZT) for signal sensing and a MetaWearC microcontroller for signal acquisition and Bluetooth transmission. Algorithms for signal processing were developed based on previous works for extracting ECG, blood pressure, heart rate, and respiration parameters.
2：Sample Selection and Data Sources:
Fifteen participants (10 females, 5 males, ages 23-36) were recruited for usability testing. For validation, signals were compared with commercial devices: a 3-lead ECG sensor from eHealth sensor kit and a Zephyr belt-type BioHarness sensor.
3：List of Experimental Equipment and Materials:
Piezoelectric sensor (muRata 7BB-20-6L0), MetaWearC microcontroller, double-sided adhesive tape, Android smartphone for data logging, and commercial validation sensors.
4：Experimental Procedures and Operational Workflow:
The sensor patch was adhered to the chest wall using double-sided tape. Signals were acquired simultaneously with commercial devices, transmitted via BLE to a mobile app, and logged. Data was analyzed using MATLAB for signal processing and parameter extraction.
5：Data Analysis Methods:
Signals were sampled at 100 Hz with a 10-bit A/D converter. Custom algorithms were used to extract heart rate, respiration rate, ECG waveform, and blood pressure values. Performance was validated by comparing extracted values with those from commercial sensors.
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