Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion

Area of Science:

• Biomedical Engineering
• Sensor Technology
• Signal Processing

Background:

• Ballistocardiogram (BCG) signals offer a non-invasive method for physiological monitoring.
• Accurate heart rate estimation is crucial for various health applications.
• Existing BCG analysis methods can be limited by signal quality and sensor contact.

Purpose of the Study:

• To develop and validate a robust heart rate estimation method using fiber Bragg grating (FBG) sensors.
• To leverage cepstral analysis for characterizing BCG signals and extracting heart rate information.
• To enhance signal quality and measurement accuracy through sensor fusion.

Main Methods:

• Utilized an array of FBG sensors embedded in a mat to capture BCG signals.
• Applied cepstral domain signal analysis to identify dominant peaks corresponding to heart beat intervals.
• Implemented a sensor fusion technique to combine data from multiple FBG sensors.
• Conducted experiments with 10 subjects in two different postures, comparing results with electrocardiogram (ECG) ground truth.

Main Results:

• The proposed method accurately estimated heart rate from BCG signals.
• Mean error of heart rate estimation was below 1 beat per minute (BPM) when compared to ECG.
• The sensor fusion approach significantly improved the signal-to-noise ratio.
• The method demonstrated robustness against varying sensor contact conditions.

Conclusions:

• The developed FBG sensor array and cepstral analysis technique provide a promising approach for accurate and robust heart rate monitoring.
• Sensor fusion is effective in enhancing BCG signal quality and measurement reliability.
• This non-invasive method holds potential for widespread application in health and wellness monitoring.